class: inverse, title-slide <hr> Analyzing NAEP/TIMSS Data with Direct Estimation using the R ======================================================== <hr> <br> **Presenters**: Paul Bailey, Ting Zhang _July 2022_ .logo[ <img src="data:image/png;base64,#presentationFigures/airLogo.png" width="80%" height="80%" /> ] --- Workshop Goal ======================================================== <br> ## Provide participants with an overview of the methods used to analyze national and international large-scale assessment data using the R package `EdSurvey` and `Dire`. --- Outline of EdSurvey Workshop ======================================================== 1. Introduction to R, EdSurvey, and Dire 2. Data Processing and Data Manipulation 3. Hands-on practice 4. Descriptive statistics 5. Hands-on practice 6. Direct estimation with EdSurvey and Dire 7. Hands-on practice --- Course Materials ======================================================== Available here: [2022 IMPS Training Content](https://github.com/American-Institutes-for-Research/2022-IMPS-EdSurvey-Training/) --- class: inverse, section-slide Introduction to R, EdSurvey, and Dire ======================================================== <hr class="sectionSlide"> .logoSection[ <img src="data:image/png;base64,#presentationFigures/naepLogo.png" width="80%" height="80%" /> ] --- Why R? ======================================================== 1. **Free:** users can legally use and edit R package code 2. **Extensible:** large variety of contributed packages that expand its functionality 3. **Reproducible:** automated data analysis 4. **Designed by and for researchers:** robust ecosystem to translate data into analyses, visualizations, and summary reports with one software --- Why EdSurvey? ======================================================== 1. **One-stop shop** for data downloading, processing, manipulation and analysis of survey data. 2. **Automated**: Weights and complex sampling design calculations are automated following standard OECD methodology. 3. **Simple**: e.g., a regression with 80 replicate weights requires only a few lines of code. 4. **Flexible**: You can use functions that rely on EdSurvey methods or get the data and use traditional R. 5. **Minimizes memory footprint** by only reading in required data. --- Why Dire? ======================================================== 1. **Score**: Assessments with the matrix booklet design require special considerations in data analysis, e.g., IRT and multiple imputation for item responses. `Dire` provides direct estimation functions that handles analyses of these assessment data properly. 2. **Efficient**: Students’ latent proficiency distribution, as well as reporting group difference parameters, are estimated on the fly. 3. **Plausible Values Generation**: No need to rely on testing companies. Plausible values can be generated from the user-defined MML model and used for further analysis. 4. **Expanding Research Scope**: Providing the opportunity for researchers to link administrative data, aggregate data about a community from official statistics, or data from multiple surveys to open up new research questions. --- class: explainerA Basic R Infrastructure ======================================================== --- class: explainerB Basic R Infrastructure ======================================================== --- class: explainerC Basic R Infrastructure ======================================================== --- class: explainerD Basic R Infrastructure ======================================================== --- class: inverse, section-slide Get to Know the R Environment ======================================================== <hr class="sectionSlide"> .logoSection[ <img src="data:image/png;base64,#presentationFigures/naepLogo.png" width="80%" height="80%" /> ] --- Follow Along - R Scripts ======================================================== <!-- --> Follow along in [edsurvey_part1_Script.R](https://github.com/American-Institutes-for-Research/2022-IMPS-EdSurvey-Training/blob/main/edsurvey_part1_Script.R) --- Notes About Using R ======================================================== - Highlight , `ctrl` + `enter` executes code to console - Comment character is a hash ```r # this line is not executed ``` - Variables are assigned with an equals or `<-` ```r x <- 12 x ``` ``` ## [1] 12 ``` - In file names on Windows use a forward slash - `C:/` - R is case sensitive! ```r j <- 12 J ``` ``` ## Error in eval(expr, envir, enclos): object 'J' not found ``` --- Notes About Using R (cont) ======================================================== - Any command can be input with a question mark preceding it to open the help guide ```r ?mean ``` - Use the up arrow on your keyboard to copy your previous lines of code - Try tab completion, type, "data" then hit the tab key <!-- --> --- Using R Functions ======================================================== - `c()` function combines values, separated by a comma, into a vector ```r colors <- c("red", "green", "blue") colors ``` ``` ## [1] "red" "green" "blue" ``` ```r numbers <- c(1, 2, 3) numbers ``` ``` ## [1] 1 2 3 ``` - In the `EdSurvey` package we'll use vectors to combine the names of variables in our analyses --- Using R Functions ======================================================== - Arguments can be explicitly or implicitly named ```r mean(x = numbers) ``` ``` ## [1] 2 ``` ```r mean(numbers) ``` ``` ## [1] 2 ``` - Arguments are separated by commas <img src="data:image/png;base64,#presentationFigures/meanArgs2.png" width="30%" height="30%" /> --- Installing the EdSurvey Package ======================================================== - After opening up RStudio, run the following scripts in the console to download and initialize the `EdSurvey` package: ```r #install Dire 2.0.1 # you may need to get rtools install.packages("Dire") # then install devtools and EdSurvey from GitHub install.packages("devtools") devtools::install_github("American-Institutes-for-Research/edsurvey") ``` ```r # to load the package library(EdSurvey) ``` --- Learning EdSurvey ======================================================== - Reading vignettes provided in training materials ```r vignette("introduction", package="EdSurvey") ``` - R help ```r help(package = "EdSurvey") ``` - [EdSurvey eBook](https://naep-research.airprojects.org/Portals/0/EdSurvey_A_Users_Guide/_book/index.html) - [EdSurvey Website](https://www.air.org/project/nces-data-r-project-edsurvey) - [EdSurvey Github](https://github.com/American-Institutes-for-Research/EdSurvey) - [NAEP Data Training workshop](http://naep-research.airprojects.org/Opportunities/Training) --- Self-Reflection - R Functions ======================================================== **Ask yourself:** What are the arguments of the function `readNAEP()`? What are some examples of acceptable values for each argument? --- Self-Reflection - R Functions ======================================================== `readNAEP()` arguments, from R documentation (*type `?readNAEP` in the console*) <!-- --> --- class: inverse, section-slide Data Processing ======================================================== <hr class="sectionSlide"> .logoSection[ <img src="data:image/png;base64,#presentationFigures/naepLogo.png" width="80%" height="80%" /> ] --- Data Processing ======================================================== - First, read in the publicly available NAEP data from `NAEPprimer` ```r sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer")) ita <- readTIMSS("~/TIMSS/2019/", countries="ita", grade=8) ``` ``` ## Found cached Grade 8 data for country code "ita: Italy". ## edsurvey.data.frame data level detail: ## |---DataLevel----|----Rows----|--Columns---|---MergeType----|-------MatchedRecords-------|-OK-| ## |Student | 3619| 3196| |*base level* | ✓ | ## |>School | 3619| 89|many:one |3619 of 3619 | ✓ | ## |>Teacher | 7238| 766|one:many |7238 of 7238 | ✓ | ``` <img src="data:image/png;base64,#presentationFigures/sdf.png" width="75%" height="75%" /> - How long did that take? - Try it again, then how long? --- Data Processing ======================================================== - Can also read in other restricted-use NAEP data by naming the file location ```r math17 <- readNAEP("//path_to_directory/Data/M48NT2AT.dat") ``` - The first character indicates the subject - `M` (Math) - The second and third characters indicate the NAEP year - `48` (2017 - 1969 = 48) - The fourth character indicates the component - `N` (National) - The fifth character indicates the type of data - `T` (Student) - The sixth character indicates the grade cohort - `2` (8th) - The seventh and eighth characters indicate the sample - `AT` (Main NAEP) --- Data Processing ======================================================== NOTE: the dat file requires its intact data folder directory in order to be read in correctly; containing both the student and school level files to merge data <img src="data:image/png;base64,#presentationFigures/dataPathway2.png" width="60%" height="60%" /> --- class: inverse, section-slide Meet Your Data ======================================================== <hr class="sectionSlide"> .logoSection[ <img src="data:image/png;base64,#presentationFigures/naepLogo.png" width="80%" height="80%" /> ] --- Quick Terminology Notes ======================================================== The `edsurvey.data.frame` class stores information about survey data via a data connection, which allows for: - Correct calculation of relevant statistics. - Limited working memory usage. <br> **`_____SDF`** The Edsurvey package uses the acronym `SDF` in the names of several functions to signify their relationship to **S**urvey **D**ata **F**rames. --- Quick Terminology Notes ======================================================== The `edsurvey.data.frame.list` class stores a list of `edsurvey.data.frame` objects. - The list can be passed to the analysis functions, and a result list will be returned. - A list can store both `cov` (covariants) and `labels` arguments. For example, the 'year' and 'country' might vary across the `edsurvey.data.frame`s in the list. - `edsurvey.data.frame.lists` can also be constructed manually. See the `?edsurvey.data.frame.list` documentation. --- class: EdSurveyWorkflowBlank One-stop Shop for NCES Survey Analysis ======================================================== --- One-stop Shop for NCES Survey Analysis ======================================================== <img src="data:image/png;base64,#presentationFigures/workflow1.png" width="60%" height="60%" /> - Install R and EdSurvey - Download and read-in data _Example functions_: - `readTIMSS` - `downloadTIMSS` --- One-stop Shop for NCES Survey Analysis ======================================================== <img src="data:image/png;base64,#presentationFigures/workflow2.png" width="60%" height="60%" /> - *Explore*: explore the codebook, see the variables with plausible values, see weights - *Search*: search variables - *Expand*: see variable levels, tabulate response percentages, see assessment scores by response category, summarize continuous variables _Example functions_: - `showCodebook`, `showPlausibleValues`, `showWeights` - `searchSDF`, `levelsSDF` - `summary2`, `edsurveyTable` --- One-stop Shop for NCES Survey Analysis ======================================================== <img src="data:image/png;base64,#presentationFigures/workflow3.png" width="60%" height="60%" /> In *EdSurvey*: Clean and manipulate data with built-in subset and recode features. In *R*: Extract and manipulate data as a data frame (for experienced users) _Example functions_: - `subset` - `rename.sdf`, `recode.sdf` - `getData`, `rebindAttributes` --- One-stop Shop for NCES Survey Analysis ======================================================== <img src="data:image/png;base64,#presentationFigures/workflow4.png" width="60%" height="60%" /> - *Run analyses*: such as regression analysis, logit analysis, mixed models, show gaps, calculate achievement levels, correlate variables, calculate percentiles. _Example functions_: - `achievementLevels`, `percentile` - `cor.sdf` - `gap` - `lm.sdf`, `glm.sdf` - `mvrlm.sdf`, `mixed.sdf` --- One-stop Shop for NCES Survey Analysis ======================================================== <img src="data:image/png;base64,#presentationFigures/EdSurveyWorkflowFull.png" width="50%" height="50%" /> - Related Documentation - [EdSurvey.pdf](https://www.air.org/sites/default/files/Edsurvey.pdf), [Chap 3, EdSurvey Book](https://naep-research.airprojects.org/Portals/0/EdSurvey_A_Users_Guide/_book/philosophyOfAnalysis.html) --- Meet Your Data - print ======================================================== **`print()`** - Print returns detailed data file information: .codeScroll20[ ```r print(ita) ``` ``` ## edsurvey.data.frame for 2019 TIMSS (Mathematics and Science) in Italy ## Dimensions: 7238 rows and 4052 columns. ## ## There are 4 full sample weights in this edsurvey.data.frame: ## 'totwgt' with 150 JK replicate weights (the default). ## ## 'tchwgt' with 150 JK replicate weights. ## ## 'matwgt' with 150 JK replicate weights. ## ## 'sciwgt' with 150 JK replicate weights. ## ## ## There are 16 subject scale(s) or subscale(s) in this edsurvey.data.frame: ## 'mmat' subject scale or subscale with 5 plausible values (the default). ## ## 'ssci' subject scale or subscale with 5 plausible values. ## ## 'malg' subject scale or subscale with 5 plausible values. ## ## 'mapp' subject scale or subscale with 5 plausible values. ## ## 'mdat' subject scale or subscale with 5 plausible values. ## ## 'mgeo' subject scale or subscale with 5 plausible values. ## ## 'mkno' subject scale or subscale with 5 plausible values. ## ## 'mnum' subject scale or subscale with 5 plausible values. ## ## 'mrea' subject scale or subscale with 5 plausible values. ## ## 'sapp' subject scale or subscale with 5 plausible values. ## ## 'sbio' subject scale or subscale with 5 plausible values. ## ## 'sche' subject scale or subscale with 5 plausible values. ## ## 'sear' subject scale or subscale with 5 plausible values. ## ## 'skno' subject scale or subscale with 5 plausible values. ## ## 'sphy' subject scale or subscale with 5 plausible values. ## ## 'srea' subject scale or subscale with 5 plausible values. ## ## ## Omitted Levels: 'Multiple', 'NA', 'OMITTED', 'OMITTED OR INVALID', 'OMITTED (BLANK ONLY)', 'BLANK(OMITTED)', ## 'BLANK(MISSING)', 'BLANK(MISS)', 'MIS.', 'MISS.', 'N. REA.', 'N.REA.', 'N. ADM.', 'N. ADMIN.', 'NOT ## ADMIN', 'NOT APPL', 'NOT ADMINISTERED', 'NOT REACHED', 'NOT ADMIN.', 'NOT APPLICABLE', 'LOGICALLY ## NOT APPLICABLE', 'MISSING', and '(Missing)' ## Achievement Levels: ## Low International Benchmark: 400 ## Intermediate International Benchmark: 475 ## High International Benchmark: 550 ## Advanced International Benchmark: 625 ``` ] <br> --- Meet Your Data - dim ======================================================== **`dim()`** - Returns the dimensions of the student data set: ```r dim(ita) ``` ``` ## [1] 7238 4052 ``` --- Meet Your Data - colnames ======================================================== **`colnames()`** - Prints the names of all variables in the student and school data sets: .codeScroll20[.code60[ ```r colnames(ita) ``` ``` ## [1] "ROWID" "idcntry" "idbook" "idschool" "idclass" "idstud" "idpop" ## [8] "mp52024" "mp52058a" "mp52058b" "mp52125" "mp52229" "mp52063" "mp52072" ## [15] "mp52146a" "mp52146b" "mp52092" "mp52046" "mp52083" "mp52082" "mp52161" ## [22] "mp52418a" "mp52418b" "mp72007a" "mp72007b" "mp72007c" "mp72007d" "mp72007e" ## [29] "mp72007" "mp72025" "mp72017" "mp72190" "mp72068" "mp72076" "mp72056" ## [36] "mp72098" "mp72103" "mp72121" "mp72180" "mp72198a" "mp72198b" "mp72198" ## [43] "mp72227" "mp72170" "mp72209" "mp62005" "mp62139" "mp62164" "mp62142" ## [50] "mp62084" "mp62351" "mp62223" "mp62027" "mp62174" "mp62244" "mp62261" ## [57] "mp62300" "mp62254" "mp62132a" "mp62132b" "mp72178" "mp72234" "mp72020" ## [64] "mp72027" "mp72052a" "mp72052b" "mp72052" "mp72067" "mp72083a" "mp72083b" ## [71] "mp72108a" "mp72108b" "mp72181" "mp72126" "mp72164a" "mp72164b" "mp72164c" ## [78] "mp72164d" "mp72164e" "mp72164" "mp72185a" "mp72185b" "mp52413" "mp52134" ## [85] "mp52078" "mp52034" "mp52174a" "mp52174b" "mp52130" "mp52073" "mp52110" ## [92] "mp52105" "mp52407" "mp52036" "mp52502" "mp52117" "mp52426" "mp62150" ## [99] "mp62335" "mp62219" "mp62002" "mp62149" "mp62241" "mp62342" "mp62105" ## [106] "mp62040" "mp62288" "mp62173" "mp62133" "mp62123a" "mp62123b" "mp52079" ## [113] "mp52204" "mp52364" "mp52215" "mp52147" "mp52067" "mp52068" "mp52087" ## [120] "mp52048" "mp52039" "mp52208" "mp52419a" "mp52419b" "mp52115" "mp52421" ## [127] "mp72002" "mp72188" "mp72035" "mp72055" "mp72222" "mp72090" "mp72233" ## [134] "mp72106a" "mp72106b" "mp72106c" "mp72128a" "mp72128b" "mp72119" "mp72153a" ## [141] "mp72153b" "mp72172" "mp62329" "mp62151" "mp62346" "mp62212" "mp62056" ## [148] "mp62317" "mp62350" "mp62078" "mp62284" "mp62245" "mp62287" "mp62345a" ## [155] "mp62345ba" "mp62345bb" "mp62345bc" "mp62345bd" "mp62345b" "mp62115" "mp72187" ## [162] "mp72022" "mp72038" "mp72045" "mp72049" "mp72069" "mp72074" "mp72013" ## [169] "mp72095a" "mp72095b" "mp72095" "mp72109" "mp72125" "mp72196" "mp72237" ## [176] "mp72232a" "mp72232b" "mp72232c" "mp72232d" "mp72232" "mp72206" "mp62271" ## [183] "mp62152" "mp62215" "mp62143" "mp62230" "mp62095" "mp62076" "mp62030" ## [190] "mp62171" "mp62301" "mp62194" "mp62344" "mp62320" "mp62296" "mp72001" ## [197] "mp72019" "mp72189" "mp72024" "mp72043" "mp72221" "mp72220" "mp72225a" ## [204] "mp72225b" "mp72225" "mp72110a" "mp72110b" "mp72150" "mp72139" "mp72229" ## [211] "mp72171" "mp72211a" "mp72211b" "mp62001" "mp62214" "mp62146" "mp62154" ## [218] "mp62067" "mp62341" "mp62242" "mp62250a" "mp62250b" "mp62170" "mp62192" ## [225] "mp62072" "mp62048a" "mp62048b" "mp62048c" "mp62048" "mp62120" "mp72005" ## [232] "mp72021" "mp72026" "mp72041a" "mp72041b" "mp72223" "mp72094" "mp72059" ## [239] "mp72080" "mp72081" "mp72140a" "mp72140b" "mp72140c" "mp72140d" "mp72140e" ## [246] "mp72140f" "mp72140" "mp72120" "mp72131" "mp72147" "mp72154" "mp72192" ## [253] "mp72161" "sp52006" "sp52069" "sp52012" "sp52021" "sp52095a" "sp52095b" ## [260] "sp52095c" "sp52095d" "sp52095z" "sp52134" "sp52054" "sp52150" "sp52243a" ## [267] "sp52243b" "sp52243c" "sp52206" "sp52112a" "sp52112b" "sp52294" "sp72072" ## [274] "sp72029" "sp72902" "sp72077" "sp72900a" "sp72900b" "sp72103" "sp72110" ## [281] "sp72130a" "sp72130b" "sp72130c" "sp72130" "sp72148" "sp72200" "sp72232a" ## [288] "sp72232b" "sp72232c" "sp72232d" "sp72232e" "sp72232f" "sp72232" "sp72275" ## [295] "sp72244" "sp72301" "sp72721" "sp72335" "sp62055" "sp62007" "sp62275" ## [302] "sp62225" "sp62111" "sp62116a" "sp62116b" "sp62116c" "sp62262" "sp62035" ## [309] "sp62144" "sp62162" "sp62233" "sp62272" "sp62171" "sp72002" "sp72403" ## [316] "sp72021" "sp72082" "sp72066" "sp72063" "sp72102" "sp72141a" "sp72141b" ## [323] "sp72921" "sp72234" "sp72251" "sp72284" "sp72345a" "sp72345b" "sp72345c" ## [330] "sp72345d" "sp72345e" "sp72345f" "sp72345g" "sp72345" "sp72349" "sp72363" ## [337] "sp52076" "sp52272" "sp52085a" "sp52085b" "sp52094" "sp52248" "sp52146" ## [344] "sp52282" "sp52299" "sp52144" "sp52214" "sp52221" "sp52101" "sp52113" ## [351] "sp52107" "sp62090" "sp62274" "sp62284" "sp62098a" "sp62098b" "sp62032" ## [358] "sp62043" "sp62158" "sp62159" "sp62005" "sp62075" "sp62004" "sp62175" ## [365] "sp62173aa" "sp62173ab" "sp62173ac" "sp62173ad" "sp62173a" "sp62173b" "sp52090a" ## [372] "sp52090b" "sp52262" "sp52267" "sp52273" "sp52015a" "sp52015b" "sp52015c" ## [379] "sp52015d" "sp52015e" "sp52015f" "sp52015z" "sp52051" "sp52026" "sp52130" ## [386] "sp52028" "sp52189" "sp52217" "sp52038" "sp52099" "sp52118" "sp72070" ## [393] "sp72400" "sp72024" "sp72462" "sp72443" "sp72903" "sp72145" "sp72100" ## [400] "sp72133" "sp72137" "sp72298" "sp72215" "sp72260" "sp72265a" "sp72265b" ## [407] "sp72265c" "sp72265d" "sp72265e" "sp72265" "sp72347" "sp72351" "sp72367" ## [414] "sp62099" "sp62095" "sp62106" "sp62064" "sp62132" "sp62163" "sp62153" ## [421] "sp62018a" "sp62018b" "sp62018c" "sp62018d" "sp62018e" "sp62018" "sp62143" ## [428] "sp62276" "sp62050" "sp62205" "sp62190" "sp62024a" "sp62024b" "sp72033" ## [435] "sp72440" "sp72032" "sp72031" "sp72086" "sp72005" "sp72048" "sp72123" ## [442] "sp72116" "sp72920" "sp72294" "sp72231" "sp72261a" "sp72261b" "sp72261c" ## [449] "sp72261d" "sp72261e" "sp72261" "sp72220" "sp72348" "sp72720" "sp62279" ## [456] "sp62112" "sp62119" "sp62093" "sp62089" "sp62006" "sp62067" "sp62247" ## [463] "sp62177" "sp62186" "sp62211a" "sp62211b" "sp62036" "sp62033" "sp62037" ## [470] "sp62242a" "sp62242b" "sp62242c" "sp62242d" "sp62242e" "sp62242" "sp72078" ## [477] "sp72460" "sp72000" "sp72906a" "sp72906b" "sp72906c" "sp72906d" "sp72906e" ## [484] "sp72906" "sp72901" "sp72038" "sp72120" "sp72143" "sp72523" "sp72168" ## [491] "sp72205" "sp72293" "sp72280a" "sp72280b" "sp72370" "sp72329" "sp62091a" ## [498] "sp62091b" "sp62100" "sp62097" "sp62101" "sp62266" "sp62128" "sp62047a" ## [505] "sp62047b" "sp62047c" "sp62047" "sp62042" "sp62250" "sp62246" "sp62056" ## [512] "sp62235" "sp62180" "sp62022a" "sp62022b" "sp62022c" "sp62022d" "sp62022" ## [519] "sp62243" "sp72011" "sp72905" "sp72049" "sp72016a" "sp72016b" "sp72016" ## [526] "sp72451" "sp72074" "sp72091" "sp72109" "sp72140" "sp72132" "sp72209" ## [533] "sp72210" "sp72249" "sp72323" "sp72368" "sp72303" "me52024" "me52058a" ## [540] "me52058b" "me52125" "me52229" "me52063" "me52072" "me52146a" "me52146b" ## [547] "me52092" "me52046" "me52083" "me52082" "me52161" "me52418a" "me52418b" ## [554] "me72007a" "me72007b" "me72007c" "me72007d" "me72007e" "me72007" "me72025" ## [561] "me72017" "me72190" "me72068" "me72076" "me72056" "me72098" "me72103" ## [568] "me72121" "me72180a" "me72180b" "me72180c" "me72180" "me72198a" "me72198b" ## [575] "me72198" "me72227" "me72170a" "me72170b" "me72170c" "me72170" "me72209" ## [582] "me62005" "me62139" "me62164" "me62142" "me62084" "me62351" "me62223" ## [589] "me62027" "me62174" "me62244a" "me62244b" "me62244" "me62261" "me62300" ## [596] "me62254" "me62132a" "me62132b" "me72178a" "me72178b" "me72178c" "me72178d" ## [603] "me72178e" "me72178" "me72234" "me72020a" "me72020b" "me72020c" "me72020d" ## [610] "me72020" "me72027" "me72052a" "me72052b" "me72052" "me72067" "me72083a" ## [617] "me72083b" "me72108a" "me72108b" "me72181" "me72126" "me72164a" "me72164b" ## [624] "me72164c" "me72164d" "me72164e" "me72164" "me72185a" "me72185b" "me52413" ## [631] "me52134" "me52078" "me52034" "me52174a" "me52174b" "me52130" "me52073" ## [638] "me52110" "me52105" "me52407" "me52036" "me52502a" "me52502b" "me52502c" ## [645] "me52502d" "me52502" "me52117" "me52426" "me62150" "me62335" "me62219" ## [652] "me62002" "me62149" "me62241" "me62342" "me62105" "me62040" "me62288a" ## [659] "me62288b" "me62288" "me62173" "me62133" "me62123a" "me62123b" "me52079" ## [666] "me52204" "me52364" "me52215" "me52147" "me52067" "me52068" "me52087a" ## [673] "me52087b" "me52087" "me52048" "me52039" "me52208" "me52419a" "me52419b" ## [680] "me52115" "me52421" "me72002" "me72188" "me72035" "me72055a" "me72055b" ## [687] "me72055c" "me72055d" "me72055e" "me72055f" "me72055" "me72222" "me72090" ## [694] "me72233" "me72106a" "me72106b" "me72106c" "me72128a" "me72128b" "me72119" ## [701] "me72153a" "me72153b" "me72172" "me62329" "me62151" "me62346" "me62212" ## [708] "me62056" "me62317a" "me62317b" "me62317c" "me62317" "me62350" "me62078" ## [715] "me62284" "me62245" "me62287" "me62345aa" "me62345ab" "me62345ac" "me62345ad" ## [722] "me62345a" "me62345ba" "me62345bb" "me62345bc" "me62345bd" "me62345b" "me62115" ## [729] "me72187" "me72022" "me72038" "me72045" "me72049" "me72069" "me72074" ## [736] "me72013" "me72095a" "me72095b" "me72095" "me72109" "me72125" "me72196" ## [743] "me72237" "me72232a" "me72232b" "me72232c" "me72232d" "me72232" "me72206" ## [750] "me62271" "me62152" "me62215a" "me62215b" "me62215" "me62143" "me62230" ## [757] "me62095" "me62076" "me62030" "me62171" "me62301" "me62194" "me62344" ## [764] "me62320" "me62296" "me72001" "me72019" "me72189" "me72024" "me72043" ## [771] "me72221" "me72220" "me72225a" "me72225b" "me72225" "me72110a" "me72110b" ## [778] "me72150" "me72139" "me72229" "me72171" "me72211a" "me72211b" "me62001" ## [785] "me62214" "me62146" "me62154" "me62067" "me62341" "me62242" "me62250a" ## [792] "me62250b" "me62170a" "me62170b" "me62170" "me62192" "me62072" "me62048a" ## [799] "me62048b" "me62048c" "me62048" "me62120" "me72005" "me72021" "me72026" ## [806] "me72041a" "me72041b" "me72223" "me72094" "me72059" "me72080" "me72081a" ## [813] "me72081b" "me72081c" "me72081d" "me72081" "me72140a" "me72140b" "me72140c" ## [820] "me72140d" "me72140e" "me72140f" "me72140" "me72120" "me72131" "me72147" ## [827] "me72154" "me72192" "me72161" "se52006" "se52069" "se52012" "se52021" ## [834] "se52095b" "se52095c" "se52095d" "se52095z" "se52134" "se52054" "se52150" ## [841] "se52243a" "se52243b" "se52243c" "se52206" "se52112a" "se52112b" "se52294" ## [848] "se72072" "se72029" "se72902" "se72077" "se72900a" "se72900b" "se72103" ## [855] "se72110" "se72130a" "se72130b" "se72130c" "se72130" "se72148" "se72200" ## [862] "se72232a" "se72232b" "se72232c" "se72232d" "se72232e" "se72232f" "se72232" ## [869] "se72275" "se72244" "se72301" "se72721" "se72335" "se62055" "se62007" ## [876] "se62275" "se62225" "se62111" "se62116a" "se62116b" "se62116c" "se62262" ## [883] "se62035" "se62144" "se62162" "se62233" "se62272" "se62171" "se72002" ## [890] "se72403a" "se72403b" "se72403c" "se72403d" "se72403" "se72021" "se72082" ## [897] "se72066" "se72063" "se72102" "se72141a" "se72141b" "se72921" "se72234" ## [904] "se72251" "se72284" "se72345a" "se72345b" "se72345c" "se72345d" "se72345e" ## [911] "se72345f" "se72345g" "se72345" "se72349" "se72363" "se52076" "se52272" ## [918] "se52085a" "se52085b" "se52094" "se52248" "se52146" "se52282" "se52299" ## [925] "se52144" "se52214" "se52221" "se52101" "se52113" "se52107" "se62090" ## [932] "se62274" "se62284" "se62098a" "se62098b" "se62032" "se62043" "se62158" ## [939] "se62159" "se62005" "se62075" "se62004" "se62175" "se62173aa" "se62173ab" ## [946] "se62173ac" "se62173ad" "se62173a" "se62173b" "se52090a" "se52090b" "se52262" ## [953] "se52267" "se52273" "se52015a" "se52015b" "se52015c" "se52015d" "se52015e" ## [960] "se52015f" "se52015z" "se52051" "se52026" "se52130" "se52028" "se52189" ## [967] "se52217" "se52038" "se52099" "se52118" "se72070" "se72400a" "se72400b" ## [974] "se72400c" "se72400d" "se72400" "se72024" "se72462" "se72443" "se72903" ## [981] "se72145" "se72100" "se72133" "se72137" "se72298" "se72215" "se72260a" ## [988] "se72260b" "se72260c" "se72260d" "se72260e" "se72260f" "se72260g" "se72260h" ## [995] "se72260" "se72265a" "se72265b" "se72265c" "se72265d" "se72265e" "se72265" ## [1002] "se72347" "se72351" "se72367" "se62099" "se62095" "se62106" "se62064" ## [1009] "se62132" "se62163" "se62153" "se62018a" "se62018b" "se62018c" "se62018d" ## [1016] "se62018e" "se62018" "se62143" "se62276" "se62050" "se62205" "se62190" ## [1023] "se62024a" "se62024b" "se72033a" "se72033b" "se72033c" "se72033d" "se72033e" ## [1030] "se72033" "se72440" "se72032" "se72031" "se72086a" "se72086b" "se72086c" ## [1037] "se72086d" "se72086" "se72005" "se72048" "se72123" "se72116" "se72920" ## [1044] "se72294" "se72231" "se72261a" "se72261b" "se72261c" "se72261d" "se72261e" ## [1051] "se72261" "se72220" "se72348" "se72720" "se62279" "se62112" "se62119" ## [1058] "se62093" "se62089" "se62006a" "se62006b" "se62006c" "se62006" "se62067" ## [1065] "se62247" "se62177" "se62186" "se62211a" "se62211b" "se62036" "se62033" ## [1072] "se62037" "se62242a" "se62242b" "se62242c" "se62242d" "se62242e" "se62242" ## [1079] "se72078" "se72460" "se72000a" "se72000b" "se72000c" "se72000d" "se72000e" ## [1086] "se72000" "se72906a" "se72906b" "se72906c" "se72906d" "se72906e" "se72906" ## [1093] "se72901" "se72038" "se72120" "se72143a" "se72143b" "se72143c" "se72143d" ## [1100] "se72143" "se72523" "se72168" "se72205" "se72293" "se72280a" "se72280b" ## [1107] "se72370" "se72329" "se62091a" "se62091b" "se62100" "se62097" "se62101a" ## [1114] "se62101b" "se62101c" "se62101d" "se62101" "se62266" "se62128" "se62047a" ## [1121] "se62047b" "se62047c" "se62047" "se62042a" "se62042b" "se62042c" "se62042d" ## [1128] "se62042" "se62250" "se62246" "se62056" "se62235" "se62180" "se62022a" ## [1135] "se62022b" "se62022c" "se62022d" "se62022" "se62243a" "se62243b" "se62243c" ## [1142] "se62243d" "se62243" "se72011" "se72905a" "se72905b" "se72905c" "se72905d" ## [1149] "se72905" "se72049" "se72016a" "se72016b" "se72016" "se72451" "se72074" ## [1156] "se72091" "se72109" "se72140" "se72132" "se72209" "se72210" "se72249" ## [1163] "se72323" "se72368" "se72303" "bnrgcal1" "bnrgcal2" "idgrader" "idgrade" ## [1170] "itlang_sa" "lcid_sa" "itsex" "bsdage" "itadmini" "ilreliab" "totwgt" ## [1177] "houwgt" "senwgt" "wgtadj1" "wgtadj2" "wgtadj3" "wgtfac1" "wgtfac2" ## [1184] "wgtfac3" "jkrep" "jkzone" "bsmmat01" "bsmmat02" "bsmmat03" "bsmmat04" ## [1191] "bsmmat05" "bsssci01" "bsssci02" "bsssci03" "bsssci04" "bsssci05" "bsmalg01" ## [1198] "bsmalg02" "bsmalg03" "bsmalg04" "bsmalg05" "bsmapp01" "bsmapp02" "bsmapp03" ## [1205] "bsmapp04" "bsmapp05" "bsmdat01" "bsmdat02" "bsmdat03" "bsmdat04" "bsmdat05" ## [1212] "bsmgeo01" "bsmgeo02" "bsmgeo03" "bsmgeo04" "bsmgeo05" "bsmkno01" "bsmkno02" ## [1219] "bsmkno03" "bsmkno04" "bsmkno05" "bsmnum01" "bsmnum02" "bsmnum03" "bsmnum04" ## [1226] "bsmnum05" "bsmrea01" "bsmrea02" "bsmrea03" "bsmrea04" "bsmrea05" "bssapp01" ## [1233] "bssapp02" "bssapp03" "bssapp04" "bssapp05" "bssbio01" "bssbio02" "bssbio03" ## [1240] "bssbio04" "bssbio05" "bssche01" "bssche02" "bssche03" "bssche04" "bssche05" ## [1247] "bssear01" "bssear02" "bssear03" "bssear04" "bssear05" "bsskno01" "bsskno02" ## [1254] "bsskno03" "bsskno04" "bsskno05" "bssphy01" "bssphy02" "bssphy03" "bssphy04" ## [1261] "bssphy05" "bssrea01" "bssrea02" "bssrea03" "bssrea04" "bssrea05" "bsmibm01" ## [1268] "bsmibm02" "bsmibm03" "bsmibm04" "bsmibm05" "bssibm01" "bssibm02" "bssibm03" ## [1275] "bssibm04" "bssibm05" "part1timeflag" "part2timeflag" "me52024_f" "me52058_f" "me52125_f" ## [1282] "me52229_f" "me52063_f" "me52072_f" "me52146_f" "me52092_f" "me52046_f" "me52083_f" ## [1289] "me52082_f" "me52161_f" "me52418_f" "me72007_f" "me72025_f" "me72017_f" "me72190_f" ## [1296] "me72068_f" "me72076_f" "me72056_f" "me72098_f" "me72103_f" "me72121_f" "me72180_f" ## [1303] "me72198_f" "me72227_f" "me72170_f" "me72209_f" "me62005_f" "me62139_f" "me62164_f" ## [1310] "me62142_f" "me62084_f" "me62351_f" "me62223_f" "me62027_f" "me62174_f" "me62244_f" ## [1317] "me62261_f" "me62300_f" "me62254_f" "me62132_f" "me72178_f" "me72234_f" "me72020_f" ## [1324] "me72027_f" "me72052_f" "me72067_f" "me72083_f" "me72108_f" "me72181_f" "me72126_f" ## [1331] "me72164_f" "me72185_f" "me52413_f" "me52134_f" "me52078_f" "me52034_f" "me52174_f" ## [1338] "me52130_f" "me52073_f" "me52110_f" "me52105_f" "me52407_f" "me52036_f" "me52502_f" ## [1345] "me52117_f" "me52426_f" "me62150_f" "me62335_f" "me62219_f" "me62002_f" "me62149_f" ## [1352] "me62241_f" "me62342_f" "me62105_f" "me62040_f" "me62288_f" "me62173_f" "me62133_f" ## [1359] "me62123_f" "me52079_f" "me52204_f" "me52364_f" "me52215_f" "me52147_f" "me52067_f" ## [1366] "me52068_f" "me52087_f" "me52048_f" "me52039_f" "me52208_f" "me52419_f" "me52115_f" ## [1373] "me52421_f" "me72002_f" "me72188_f" "me72035_f" "me72055_f" "me72222_f" "me72090_f" ## [1380] "me72233_f" "me72106_f" "me72128_f" "me72119_f" "me72153_f" "me72172_f" "me62329_f" ## [1387] "me62151_f" "me62346_f" "me62212_f" "me62056_f" "me62317_f" "me62350_f" "me62078_f" ## [1394] "me62284_f" "me62245_f" "me62287_f" "me62345_f" "me62115_f" "me72187_f" "me72022_f" ## [1401] "me72038_f" "me72045_f" "me72049_f" "me72069_f" "me72074_f" "me72013_f" "me72095_f" ## [1408] "me72109_f" "me72125_f" "me72196_f" "me72237_f" "me72232_f" "me72206_f" "me62271_f" ## [1415] "me62152_f" "me62215_f" "me62143_f" "me62230_f" "me62095_f" "me62076_f" "me62030_f" ## [1422] "me62171_f" "me62301_f" "me62194_f" "me62344_f" "me62320_f" "me62296_f" "me72001_f" ## [1429] "me72019_f" "me72189_f" "me72024_f" "me72043_f" "me72221_f" "me72220_f" "me72225_f" ## [1436] "me72110_f" "me72150_f" "me72139_f" "me72229_f" "me72171_f" "me72211_f" "me62001_f" ## [1443] "me62214_f" "me62146_f" "me62154_f" "me62067_f" "me62341_f" "me62242_f" "me62250_f" ## [1450] "me62170_f" "me62192_f" "me62072_f" "me62048_f" "me62120_f" "me72005_f" "me72021_f" ## [1457] "me72026_f" "me72041_f" "me72223_f" "me72094_f" "me72059_f" "me72080_f" "me72081_f" ## [1464] "me72140_f" "me72120_f" "me72131_f" "me72147_f" "me72154_f" "me72192_f" "me72161_f" ## [1471] "se52006_f" "se52069_f" "se52012_f" "se52021_f" "se52095_f" "se52134_f" "se52054_f" ## [1478] "se52150_f" "se52243_f" "se52206_f" "se52112_f" "se52294_f" "se72072_f" "se72029_f" ## [1485] "se72902_f" "se72077_f" "se72900_f" "se72103_f" "se72110_f" "se72130_f" "se72148_f" ## [1492] "se72200_f" "se72232_f" "se72275_f" "se72244_f" "se72301_f" "se72721_f" "se72335_f" ## [1499] "se62055_f" "se62007_f" "se62275_f" "se62225_f" "se62111_f" "se62116_f" "se62262_f" ## [1506] "se62035_f" "se62144_f" "se62162_f" "se62233_f" "se62272_f" "se62171_f" "se72002_f" ## [1513] "se72403_f" "se72021_f" "se72082_f" "se72066_f" "se72063_f" "se72102_f" "se72141_f" ## [1520] "se72921_f" "se72234_f" "se72251_f" "se72284_f" "se72345_f" "se72349_f" "se72363_f" ## [1527] "se52076_f" "se52272_f" "se52085_f" "se52094_f" "se52248_f" "se52146_f" "se52282_f" ## [1534] "se52299_f" "se52144_f" "se52214_f" "se52221_f" "se52101_f" "se52113_f" "se52107_f" ## [1541] "se62090_f" "se62274_f" "se62284_f" "se62098_f" "se62032_f" "se62043_f" "se62158_f" ## [1548] "se62159_f" "se62005_f" "se62075_f" "se62004_f" "se62175_f" "se62173_f" "se52090_f" ## [1555] "se52262_f" "se52267_f" "se52273_f" "se52015_f" "se52051_f" "se52026_f" "se52130_f" ## [1562] "se52028_f" "se52189_f" "se52217_f" "se52038_f" "se52099_f" "se52118_f" "se72070_f" ## [1569] "se72400_f" "se72024_f" "se72462_f" "se72443_f" "se72903_f" "se72145_f" "se72100_f" ## [1576] "se72133_f" "se72137_f" "se72298_f" "se72215_f" "se72260_f" "se72265_f" "se72347_f" ## [1583] "se72351_f" "se72367_f" "se62099_f" "se62095_f" "se62106_f" "se62064_f" "se62132_f" ## [1590] "se62163_f" "se62153_f" "se62018_f" "se62143_f" "se62276_f" "se62050_f" "se62205_f" ## [1597] "se62190_f" "se62024_f" "se72033_f" "se72440_f" "se72032_f" "se72031_f" "se72086_f" ## [1604] "se72005_f" "se72048_f" "se72123_f" "se72116_f" "se72920_f" "se72294_f" "se72231_f" ## [1611] "se72261_f" "se72220_f" "se72348_f" "se72720_f" "se62279_f" "se62112_f" "se62119_f" ## [1618] "se62093_f" "se62089_f" "se62006_f" "se62067_f" "se62247_f" "se62177_f" "se62186_f" ## [1625] "se62211_f" "se62036_f" "se62033_f" "se62037_f" "se62242_f" "se72078_f" "se72460_f" ## [1632] "se72000_f" "se72906_f" "se72901_f" "se72038_f" "se72120_f" "se72143_f" "se72523_f" ## [1639] "se72168_f" "se72205_f" "se72293_f" "se72280_f" "se72370_f" "se72329_f" "se62091_f" ## [1646] "se62100_f" "se62097_f" "se62101_f" "se62266_f" "se62128_f" "se62047_f" "se62042_f" ## [1653] "se62250_f" "se62246_f" "se62056_f" "se62235_f" "se62180_f" "se62022_f" "se62243_f" ## [1660] "se72011_f" "se72905_f" "se72049_f" "se72016_f" "se72451_f" "se72074_f" "se72091_f" ## [1667] "se72109_f" "se72140_f" "se72132_f" "se72209_f" "se72210_f" "se72249_f" "se72323_f" ## [1674] "se72368_f" "se72303_f" "me52024_s" "me52058_s" "me52125_s" "me52229_s" "me52063_s" ## [1681] "me52072_s" "me52146_s" "me52092_s" "me52046_s" "me52083_s" "me52082_s" "me52161_s" ## [1688] "me52418_s" "me72007_s" "me72025_s" "me72017_s" "me72190_s" "me72068_s" "me72076_s" ## [1695] "me72056_s" "me72098_s" "me72103_s" "me72121_s" "me72180_s" "me72198_s" "me72227_s" ## [1702] "me72170_s" "me72209_s" "me62005_s" "me62139_s" "me62164_s" "me62142_s" "me62084_s" ## [1709] "me62351_s" "me62223_s" "me62027_s" "me62174_s" "me62244_s" "me62261_s" "me62300_s" ## [1716] "me62254_s" "me62132_s" "me72178_s" "me72234_s" "me72020_s" "me72027_s" "me72052_s" ## [1723] "me72067_s" "me72083_s" "me72108_s" "me72181_s" "me72126_s" "me72164_s" "me72185_s" ## [1730] "me52413_s" "me52134_s" "me52078_s" "me52034_s" "me52174_s" "me52130_s" "me52073_s" ## [1737] "me52110_s" "me52105_s" "me52407_s" "me52036_s" "me52502_s" "me52117_s" "me52426_s" ## [1744] "me62150_s" "me62335_s" "me62219_s" "me62002_s" "me62149_s" "me62241_s" "me62342_s" ## [1751] "me62105_s" "me62040_s" "me62288_s" "me62173_s" "me62133_s" "me62123_s" "me52079_s" ## [1758] "me52204_s" "me52364_s" "me52215_s" "me52147_s" "me52067_s" "me52068_s" "me52087_s" ## [1765] "me52048_s" "me52039_s" "me52208_s" "me52419_s" "me52115_s" "me52421_s" "me72002_s" ## [1772] "me72188_s" "me72035_s" "me72055_s" "me72222_s" "me72090_s" "me72233_s" "me72106_s" ## [1779] "me72128_s" "me72119_s" "me72153_s" "me72172_s" "me62329_s" "me62151_s" "me62346_s" ## [1786] "me62212_s" "me62056_s" "me62317_s" "me62350_s" "me62078_s" "me62284_s" "me62245_s" ## [1793] "me62287_s" "me62345_s" "me62115_s" "me72187_s" "me72022_s" "me72038_s" "me72045_s" ## [1800] "me72049_s" "me72069_s" "me72074_s" "me72013_s" "me72095_s" "me72109_s" "me72125_s" ## [1807] "me72196_s" "me72237_s" "me72232_s" "me72206_s" "me62271_s" "me62152_s" "me62215_s" ## [1814] "me62143_s" "me62230_s" "me62095_s" "me62076_s" "me62030_s" "me62171_s" "me62301_s" ## [1821] "me62194_s" "me62344_s" "me62320_s" "me62296_s" "me72001_s" "me72019_s" "me72189_s" ## [1828] "me72024_s" "me72043_s" "me72221_s" "me72220_s" "me72225_s" "me72110_s" "me72150_s" ## [1835] "me72139_s" "me72229_s" "me72171_s" "me72211_s" "me62001_s" "me62214_s" "me62146_s" ## [1842] "me62154_s" "me62067_s" "me62341_s" "me62242_s" "me62250_s" "me62170_s" "me62192_s" ## [1849] "me62072_s" "me62048_s" "me62120_s" "me72005_s" "me72021_s" "me72026_s" "me72041_s" ## [1856] "me72223_s" "me72094_s" "me72059_s" "me72080_s" "me72081_s" "me72140_s" "me72120_s" ## [1863] "me72131_s" "me72147_s" "me72154_s" "me72192_s" "me72161_s" "se52006_s" "se52069_s" ## [1870] "se52012_s" "se52021_s" "se52095_s" "se52134_s" "se52054_s" "se52150_s" "se52243_s" ## [1877] "se52206_s" "se52112_s" "se52294_s" "se72072_s" "se72029_s" "se72902_s" "se72077_s" ## [1884] "se72900_s" "se72103_s" "se72110_s" "se72130_s" "se72148_s" "se72200_s" "se72232_s" ## [1891] "se72275_s" "se72244_s" "se72301_s" "se72721_s" "se72335_s" "se62055_s" "se62007_s" ## [1898] "se62275_s" "se62225_s" "se62111_s" "se62116_s" "se62262_s" "se62035_s" "se62144_s" ## [1905] "se62162_s" "se62233_s" "se62272_s" "se62171_s" "se72002_s" "se72403_s" "se72021_s" ## [1912] "se72082_s" "se72066_s" "se72063_s" "se72102_s" "se72141_s" "se72921_s" "se72234_s" ## [1919] "se72251_s" "se72284_s" "se72345_s" "se72349_s" "se72363_s" "se52076_s" "se52272_s" ## [1926] "se52085_s" "se52094_s" "se52248_s" "se52146_s" "se52282_s" "se52299_s" "se52144_s" ## [1933] "se52214_s" "se52221_s" "se52101_s" "se52113_s" "se52107_s" "se62090_s" "se62274_s" ## [1940] "se62284_s" "se62098_s" "se62032_s" "se62043_s" "se62158_s" "se62159_s" "se62005_s" ## [1947] "se62075_s" "se62004_s" "se62175_s" "se62173_s" "se52090_s" "se52262_s" "se52267_s" ## [1954] "se52273_s" "se52015_s" "se52051_s" "se52026_s" "se52130_s" "se52028_s" "se52189_s" ## [1961] "se52217_s" "se52038_s" "se52099_s" "se52118_s" "se72070_s" "se72400_s" "se72024_s" ## [1968] "se72462_s" "se72443_s" "se72903_s" "se72145_s" "se72100_s" "se72133_s" "se72137_s" ## [1975] "se72298_s" "se72215_s" "se72260_s" "se72265_s" "se72347_s" "se72351_s" "se72367_s" ## [1982] "se62099_s" "se62095_s" "se62106_s" "se62064_s" "se62132_s" "se62163_s" "se62153_s" ## [1989] "se62018_s" "se62143_s" "se62276_s" "se62050_s" "se62205_s" "se62190_s" "se62024_s" ## [1996] "se72033_s" "se72440_s" "se72032_s" "se72031_s" "se72086_s" "se72005_s" "se72048_s" ## [2003] "se72123_s" "se72116_s" "se72920_s" "se72294_s" "se72231_s" "se72261_s" "se72220_s" ## [2010] "se72348_s" "se72720_s" "se62279_s" "se62112_s" "se62119_s" "se62093_s" "se62089_s" ## [2017] "se62006_s" "se62067_s" "se62247_s" "se62177_s" "se62186_s" "se62211_s" "se62036_s" ## [2024] "se62033_s" "se62037_s" "se62242_s" "se72078_s" "se72460_s" "se72000_s" "se72906_s" ## [2031] "se72901_s" "se72038_s" "se72120_s" "se72143_s" "se72523_s" "se72168_s" "se72205_s" ## [2038] "se72293_s" "se72280_s" "se72370_s" "se72329_s" "se62091_s" "se62100_s" "se62097_s" ## [2045] "se62101_s" "se62266_s" "se62128_s" "se62047_s" "se62042_s" "se62250_s" "se62246_s" ## [2052] "se62056_s" "se62235_s" "se62180_s" "se62022_s" "se62243_s" "se72011_s" "se72905_s" ## [2059] "se72049_s" "se72016_s" "se72451_s" "se72074_s" "se72091_s" "se72109_s" "se72140_s" ## [2066] "se72132_s" "se72209_s" "se72210_s" "se72249_s" "se72323_s" "se72368_s" "se72303_s" ## [2073] "version" "scope" "bsbg01" "bsbg03" "bsbg04" "bsbg05a" "bsbg05b" ## [2080] "bsbg05c" "bsbg05d" "bsbg05e" "bsbg05f" "bsbg05g" "bsbg05h" "bsbg05i" ## [2087] "bsbg06a" "bsbg06b" "bsbg07" "bsbg08a" "bsbg08b" "bsbg09a" "bsbg09b" ## [2094] "bsbg10" "bsbg11a" "bsbg11b" "bsbg12a" "bsbg12b" "bsbg12c" "bsbg12d" ## [2101] "bsbg12e" "bsbg12f" "bsbg13a" "bsbg13b" "bsbg13c" "bsbg13d" "bsbg13e" ## [2108] "bsbg14a" "bsbg14b" "bsbg14c" "bsbg14d" "bsbg14e" "bsbg14f" "bsbg14g" ## [2115] "bsbg14h" "bsbg14i" "bsbg14j" "bsbg14k" "bsbg14l" "bsbg14m" "bsbg14n" ## [2122] "bsbm15" "bsbm16a" "bsbm16b" "bsbm16c" "bsbm16d" "bsbm16e" "bsbm16f" ## [2129] "bsbm16g" "bsbm16h" "bsbm16i" "bsbm17a" "bsbm17b" "bsbm17c" "bsbm17d" ## [2136] "bsbm17e" "bsbm17f" "bsbm17g" "bsbm18a" "bsbm18b" "bsbm18c" "bsbm18d" ## [2143] "bsbm18e" "bsbm18f" "bsbm19a" "bsbm19b" "bsbm19c" "bsbm19d" "bsbm19e" ## [2150] "bsbm19f" "bsbm19g" "bsbm19h" "bsbm19i" "bsbm20a" "bsbm20b" "bsbm20c" ## [2157] "bsbm20d" "bsbm20e" "bsbm20f" "bsbm20g" "bsbm20h" "bsbm20i" "bsbs21" ## [2164] "bsbs22a" "bsbs22b" "bsbs22c" "bsbs22d" "bsbs22e" "bsbs22f" "bsbs22g" ## [2171] "bsbs22h" "bsbs22i" "bsbs23a" "bsbs23b" "bsbs23c" "bsbs23d" "bsbs23e" ## [2178] "bsbs23f" "bsbs23g" "bsbs24a" "bsbs24b" "bsbs24c" "bsbs24d" "bsbs24e" ## [2185] "bsbs24f" "bsbs24g" "bsbs24h" "bsbs25a" "bsbs25b" "bsbs25c" "bsbs25d" ## [2192] "bsbs25e" "bsbs25f" "bsbs25g" "bsbs25h" "bsbs25i" "bsbm26aa" "bsbs26ab" ## [2199] "bsbm26ba" "bsbs26bb" "bsbm27aa" "bsbs27ab" "bsbm27ba" "bsbs27bb" "bsbb21" ## [2206] "bsbb22" "bsbb23a" "bsbb23b" "bsbb23c" "bsbb23d" "bsbb23e" "bsbb23f" ## [2213] "bsbb23g" "bsbb23h" "bsbb23i" "bsbb24a" "bsbb24b" "bsbb24c" "bsbb24d" ## [2220] "bsbb24e" "bsbb24f" "bsbb24g" "bsbb25a" "bsbb25b" "bsbb25c" "bsbb25d" ## [2227] "bsbb25e" "bsbb25f" "bsbb25g" "bsbb25h" "bsbe26" "bsbe27" "bsbe28a" ## [2234] "bsbe28b" "bsbe28c" "bsbe28d" "bsbe28e" "bsbe28f" "bsbe28g" "bsbe28h" ## [2241] "bsbe28i" "bsbe29a" "bsbe29b" "bsbe29c" "bsbe29d" "bsbe29e" "bsbe29f" ## [2248] "bsbe29g" "bsbe30a" "bsbe30b" "bsbe30c" "bsbe30d" "bsbe30e" "bsbe30f" ## [2255] "bsbe30g" "bsbe30h" "bsbc31" "bsbc32" "bsbc33a" "bsbc33b" "bsbc33c" ## [2262] "bsbc33d" "bsbc33e" "bsbc33f" "bsbc33g" "bsbc33h" "bsbc33i" "bsbc34a" ## [2269] "bsbc34b" "bsbc34c" "bsbc34d" "bsbc34e" "bsbc34f" "bsbc34g" "bsbc35a" ## [2276] "bsbc35b" "bsbc35c" "bsbc35d" "bsbc35e" "bsbc35f" "bsbc35g" "bsbc35h" ## [2283] "bsbp36" "bsbp37" "bsbp38a" "bsbp38b" "bsbp38c" "bsbp38d" "bsbp38e" ## [2290] "bsbp38f" "bsbp38g" "bsbp38h" "bsbp38i" "bsbp39a" "bsbp39b" "bsbp39c" ## [2297] "bsbp39d" "bsbp39e" "bsbp39f" "bsbp39g" "bsbp40a" "bsbp40b" "bsbp40c" ## [2304] "bsbp40d" "bsbp40e" "bsbp40f" "bsbp40g" "bsbp40h" "bsbs41a" "bsbs41b" ## [2311] "bsbs41c" "bsbs41d" "bsbs41e" "bsbs41f" "bsbs41g" "bsbs41h" "bsbs41i" ## [2318] "bsbm42aa" "bsbb42ab" "bsbe42ac" "bsbc42ad" "bsbp42ae" "bsbm42ba" "bsbb42bb" ## [2325] "bsbe42bc" "bsbc42bd" "bsbp42be" "bsbm43aa" "bsbs43ab" "bsbm43ba" "bsbs43bb" ## [2332] "bsbe01a" "bsbe01ba" "bsbe01bb" "bsbe01bc" "bsbe01bd" "bsbe01be" "bsbe01bf" ## [2339] "bsbe02a" "bsbe02b" "bsbe02c" "bsbe02d" "bsbe03a" "bsbe03b" "bsbe03c" ## [2346] "bsbe03d" "bsbe03e" "bsbe03f" "bsbe03g" "bsbe04a" "bsbe04b" "bsbe04c" ## [2353] "bsbe04d" "bsbe04e" "bsbe04f" "bsbe04g" "bsbe04h" "itlang_sq" "lcid_sq" ## [2360] "bsbgher" "bsdgher" "bsbgssb" "bsdgssb" "bsbgsb" "bsdgsb" "bsbgslm" ## [2367] "bsdgslm" "bsbgicm" "bsdgicm" "bsbgdml" "bsdgdml" "bsbgscm" "bsdgscm" ## [2374] "bsbgsvm" "bsdgsvm" "bsbgsls" "bsdgsls" "bsbgics" "bsdgics" "bsbgscs" ## [2381] "bsdgscs" "bsbgsvs" "bsdgsvs" "bsbgslb" "bsdgslb" "bsbgicb" "bsdgicb" ## [2388] "bsbgscb" "bsdgscb" "bsbgsle" "bsdgsle" "bsbgice" "bsdgice" "bsbgsce" ## [2395] "bsdgsce" "bsbgslc" "bsdgslc" "bsbgicc" "bsdgicc" "bsbgscc" "bsdgscc" ## [2402] "bsbgslp" "bsdgslp" "bsbgicp" "bsdgicp" "bsbgscp" "bsdgscp" "bsbgsec" ## [2409] "bsdgsec" "bsdg05s" "bsdgedup" "bsdmlowp" "bsdslowp" "mpr52058a" "mpr52058b" ## [2416] "mpr52229" "mpr52146a" "mpr52146b" "mpr72017" "mpr72190" "mpr72056" "mpr72098" ## [2423] "mpr72121" "mpr72180" "mpr72198a" "mpr72198b" "mpr72227" "mpr72170" "mpr72209" ## [2430] "mpr62139" "mpr62142" "mpr62027" "mpr62244" "mpr62300" "mpr62254" "mpr62132a" ## [2437] "mpr72178" "mpr72020" "mpr72052a" "mpr72052b" "mpr72083a" "mpr72108a" "mpr72108b" ## [2444] "mpr72181" "mpr72126" "mpr72185a" "mpr72185b" "mpr52174a" "mpr52174b" "mpr52110" ## [2451] "mpr52105" "mpr52036" "mpr52502" "mpr52117" "mpr62150" "mpr62002" "mpr62241" ## [2458] "mpr62105" "mpr62288" "mpr62173" "mpr52364" "mpr52215" "mpr52087" "mpr52048" ## [2465] "mpr52039" "mpr52421" "mpr72002" "mpr72035" "mpr72055" "mpr72106a" "mpr72106b" ## [2472] "mpr72106c" "mpr72128a" "mpr72128b" "mpr72119" "mpr72153a" "mpr72153b" "mpr62151" ## [2479] "mpr62346" "mpr62056" "mpr62317" "mpr62078" "mpr62287" "mpr62345a" "mpr72187" ## [2486] "mpr72045" "mpr72049" "mpr72069" "mpr72074" "mpr72095a" "mpr72095b" "mpr72109" ## [2493] "mpr72196" "mpr72206" "mpr62152" "mpr62215" "mpr62143" "mpr62030" "mpr62301" ## [2500] "mpr62344" "mpr62296" "mpr72001" "mpr72019" "mpr72024" "mpr72225a" "mpr72225b" ## [2507] "mpr72110a" "mpr72110b" "mpr72139" "mpr72229" "mpr72171" "mpr72211b" "mpr62214" ## [2514] "mpr62154" "mpr62250a" "mpr62250b" "mpr62170" "mpr62192" "mpr62072" "mpr72021" ## [2521] "mpr72026" "mpr72041a" "mpr72041b" "mpr72094" "mpr72059" "mpr72081" "mpr72120" ## [2528] "mpr72131" "mpr72147" "mpr72161" "spr52006" "spr52021" "spr52054" "spr52243a" ## [2535] "spr52243b" "spr52112b" "spr72902" "spr72900a" "spr72900b" "spr72103" "spr72110" ## [2542] "spr72244" "spr62275" "spr62111" "spr62116a" "spr62116c" "spr62116b" "spr62162" ## [2549] "spr72403" "spr72082" "spr72102" "spr72141a" "spr72141b" "spr72921" "spr72284" ## [2556] "spr52272" "spr52085b" "spr52085a" "spr52094" "spr52146" "spr52214" "spr52101" ## [2563] "spr62274" "spr62098a" "spr62098b" "spr62043" "spr62005" "spr62175" "spr52090b" ## [2570] "spr52273" "spr52051" "spr52189" "spr52099" "spr52118" "spr72400" "spr72903" ## [2577] "spr72145" "spr72298" "spr72215" "spr72260" "spr72351" "spr62095" "spr62064" ## [2584] "spr62163" "spr62143" "spr62276" "spr62050" "spr62024b" "spr72033" "spr72440" ## [2591] "spr72086" "spr72005" "spr72920" "spr72294" "spr72348" "spr62112" "spr62093" ## [2598] "spr62006" "spr62067" "spr62211b" "spr62211a" "spr62033" "spr72078" "spr72000" ## [2605] "spr72143" "spr72523" "spr72293" "spr72280a" "spr62100" "spr62101" "spr62128" ## [2612] "spr62042" "spr62250" "spr62056" "spr62243" "spr72905" "spr72016a" "spr72016b" ## [2619] "spr72451" "spr72074" "spr72109" "spr72132" "spr72210" "mer52146b" "mer72209" ## [2626] "mer62300" "mer62254" "mer72083a" "mer72108b" "mer72181" "mer52105" "mer52036" ## [2633] "mer52117" "mer62241" "mer52048" "mer52421" "mer72002" "mer72106c" "mer72119" ## [2640] "mer62346" "mer62056" "mer62287" "mer72074" "mer72109" "mer72206" "mer72110b" ## [2647] "mer72211b" "mer62072" "mer72041a" "mer72041b" "mer72131" "mer72161" "ser52006" ## [2654] "ser52021" "ser52054" "ser52243a" "ser52243b" "ser52112b" "ser72902" "ser72900a" ## [2661] "ser72900b" "ser72103" "ser72244" "ser62275" "ser62111" "ser62116a" "ser62116b" ## [2668] "ser62116c" "ser62162" "ser72082" "ser72102" "ser72141a" "ser72141b" "ser72921" ## [2675] "ser72284" "ser52272" "ser52085a" "ser52085b" "ser52094" "ser52146" "ser52214" ## [2682] "ser52101" "ser62274" "ser62098a" "ser62098b" "ser62043" "ser62005" "ser62175" ## [2689] "ser52090b" "ser52273" "ser52051" "ser52189" "ser52099" "ser52118" "ser72903" ## [2696] "ser72145" "ser72298" "ser72215" "ser72351" "ser62095" "ser62064" "ser62163" ## [2703] "ser62143" "ser62276" "ser62050" "ser62024b" "ser72005" "ser72920" "ser72294" ## [2710] "ser72348" "ser62112" "ser62093" "ser62211a" "ser62211b" "ser62033" "ser72078" ## [2717] "ser72523" "ser72293" "ser72280a" "ser62100" "ser62128" "ser62250" "ser62056" ## [2724] "ser72016a" "ser72016b" "ser72451" "ser72074" "ser72109" "ser72132" "ser72210" ## [2731] "mpi52058a" "mpi52058b" "mpi52229" "mpi52146a" "mpi52146b" "mpi72017" "mpi72190" ## [2738] "mpi72056" "mpi72098" "mpi72121" "mpi72180" "mpi72198a" "mpi72198b" "mpi72227" ## [2745] "mpi72170" "mpi72209" "mpi62139" "mpi62142" "mpi62027" "mpi62244" "mpi62300" ## [2752] "mpi62254" "mpi62132a" "mpi72178" "mpi72020" "mpi72052a" "mpi72052b" "mpi72083a" ## [2759] "mpi72108a" "mpi72108b" "mpi72181" "mpi72126" "mpi72185a" "mpi72185b" "mpi52174a" ## [2766] "mpi52174b" "mpi52110" "mpi52105" "mpi52036" "mpi52502" "mpi52117" "mpi62150" ## [2773] "mpi62002" "mpi62241" "mpi62105" "mpi62288" "mpi62173" "mpi52364" "mpi52215" ## [2780] "mpi52087" "mpi52048" "mpi52039" "mpi52421" "mpi72002" "mpi72035" "mpi72055" ## [2787] "mpi72106a" "mpi72106b" "mpi72106c" "mpi72128a" "mpi72128b" "mpi72119" "mpi72153a" ## [2794] "mpi72153b" "mpi62151" "mpi62346" "mpi62056" "mpi62317" "mpi62078" "mpi62287" ## [2801] "mpi62345a" "mpi72187" "mpi72045" "mpi72049" "mpi72069" "mpi72074" "mpi72095a" ## [2808] "mpi72095b" "mpi72109" "mpi72196" "mpi72206" "mpi62152" "mpi62215" "mpi62143" ## [2815] "mpi62030" "mpi62301" "mpi62344" "mpi62296" "mpi72001" "mpi72019" "mpi72024" ## [2822] "mpi72225a" "mpi72225b" "mpi72110a" "mpi72110b" "mpi72139" "mpi72229" "mpi72171" ## [2829] "mpi72211b" "mpi62214" "mpi62154" "mpi62250a" "mpi62250b" "mpi62170" "mpi62192" ## [2836] "mpi62072" "mpi72021" "mpi72026" "mpi72041a" "mpi72041b" "mpi72094" "mpi72059" ## [2843] "mpi72081" "mpi72120" "mpi72131" "mpi72147" "mpi72161" "spi52006" "spi52021" ## [2850] "spi52054" "spi52243a" "spi52243b" "spi52112b" "spi72902" "spi72900a" "spi72900b" ## [2857] "spi72103" "spi72110" "spi72244" "spi62275" "spi62111" "spi62116a" "spi62116c" ## [2864] "spi62116b" "spi62162" "spi72403" "spi72082" "spi72102" "spi72141a" "spi72141b" ## [2871] "spi72921" "spi72284" "spi52272" "spi52085b" "spi52085a" "spi52094" "spi52146" ## [2878] "spi52214" "spi52101" "spi62274" "spi62098a" "spi62098b" "spi62043" "spi62005" ## [2885] "spi62175" "spi52090b" "spi52273" "spi52051" "spi52189" "spi52099" "spi52118" ## [2892] "spi72400" "spi72903" "spi72145" "spi72298" "spi72215" "spi72260" "spi72351" ## [2899] "spi62095" "spi62064" "spi62163" "spi62143" "spi62276" "spi62050" "spi62024b" ## [2906] "spi72033" "spi72440" "spi72086" "spi72005" "spi72920" "spi72294" "spi72348" ## [2913] "spi62112" "spi62093" "spi62006" "spi62067" "spi62211b" "spi62211a" "spi62033" ## [2920] "spi72078" "spi72000" "spi72143" "spi72523" "spi72293" "spi72280a" "spi62100" ## [2927] "spi62101" "spi62128" "spi62042" "spi62250" "spi62056" "spi62243" "spi72905" ## [2934] "spi72016a" "spi72016b" "spi72451" "spi72074" "spi72109" "spi72132" "spi72210" ## [2941] "mei52146b" "mei72209" "mei62300" "mei62254" "mei72083a" "mei72108b" "mei72181" ## [2948] "mei52105" "mei52036" "mei52117" "mei62241" "mei52048" "mei52421" "mei72002" ## [2955] "mei72106c" "mei72119" "mei62346" "mei62056" "mei62287" "mei72074" "mei72109" ## [2962] "mei72206" "mei72110b" "mei72211b" "mei62072" "mei72041a" "mei72041b" "mei72131" ## [2969] "mei72161" "sei52006" "sei52021" "sei52054" "sei52243a" "sei52243b" "sei52112b" ## [2976] "sei72902" "sei72900a" "sei72900b" "sei72103" "sei72244" "sei62275" "sei62111" ## [2983] "sei62116a" "sei62116b" "sei62116c" "sei62162" "sei72082" "sei72102" "sei72141a" ## [2990] "sei72141b" "sei72921" "sei72284" "sei52272" "sei52085a" "sei52085b" "sei52094" ## [2997] "sei52146" "sei52214" "sei52101" "sei62274" "sei62098a" "sei62098b" "sei62043" ## [3004] "sei62005" "sei62175" "sei52090b" "sei52273" "sei52051" "sei52189" "sei52099" ## [3011] "sei52118" "sei72903" "sei72145" "sei72298" "sei72215" "sei72351" "sei62095" ## [3018] "sei62064" "sei62163" "sei62143" "sei62276" "sei62050" "sei62024b" "sei72005" ## [3025] "sei72920" "sei72294" "sei72348" "sei62112" "sei62093" "sei62211a" "sei62211b" ## [3032] "sei62033" "sei72078" "sei72523" "sei72293" "sei72280a" "sei62100" "sei62128" ## [3039] "sei62250" "sei62056" "sei72016a" "sei72016b" "sei72451" "sei72074" "sei72109" ## [3046] "sei72132" "sei72210" "jk1" "jk2" "jk3" "jk4" "jk5" ## [3053] "jk6" "jk7" "jk8" "jk9" "jk10" "jk11" "jk12" ## [3060] "jk13" "jk14" "jk15" "jk16" "jk17" "jk18" "jk19" ## [3067] "jk20" "jk21" "jk22" "jk23" "jk24" "jk25" "jk26" ## [3074] "jk27" "jk28" "jk29" "jk30" "jk31" "jk32" "jk33" ## [3081] "jk34" "jk35" "jk36" "jk37" "jk38" "jk39" "jk40" ## [3088] "jk41" "jk42" "jk43" "jk44" "jk45" "jk46" "jk47" ## [3095] "jk48" "jk49" "jk50" "jk51" "jk52" "jk53" "jk54" ## [3102] "jk55" "jk56" "jk57" "jk58" "jk59" "jk60" "jk61" ## [3109] "jk62" "jk63" "jk64" "jk65" "jk66" "jk67" "jk68" ## [3116] "jk69" "jk70" "jk71" "jk72" "jk73" "jk74" "jk75" ## [3123] "jk76" "jk77" "jk78" "jk79" "jk80" "jk81" "jk82" ## [3130] "jk83" "jk84" "jk85" "jk86" "jk87" "jk88" "jk89" ## [3137] "jk90" "jk91" "jk92" "jk93" "jk94" "jk95" "jk96" ## [3144] "jk97" "jk98" "jk99" "jk100" "jk101" "jk102" "jk103" ## [3151] "jk104" "jk105" "jk106" "jk107" "jk108" "jk109" "jk110" ## [3158] "jk111" "jk112" "jk113" "jk114" "jk115" "jk116" "jk117" ## [3165] "jk118" "jk119" "jk120" "jk121" "jk122" "jk123" "jk124" ## [3172] "jk125" "jk126" "jk127" "jk128" "jk129" "jk130" "jk131" ## [3179] "jk132" "jk133" "jk134" "jk135" "jk136" "jk137" "jk138" ## [3186] "jk139" "jk140" "jk141" "jk142" "jk143" "jk144" "jk145" ## [3193] "jk146" "jk147" "jk148" "jk149" "jk150" "bcbg03a" "bcbg03b" ## [3200] "bcbg04" "bcbg05a" "bcbg05b" "bcbg06a" "bcbg06b" "bcbg06c" "bcbg07" ## [3207] "bcbg08a" "bcbg08b" "bcbg09" "bcbg10a" "bcbg10b" "bcbg11" "bcbg12" ## [3214] "bcbg13aa" "bcbg13ab" "bcbg13ac" "bcbg13ad" "bcbg13ae" "bcbg13af" "bcbg13ag" ## [3221] "bcbg13ah" "bcbg13ai" "bcbg13ba" "bcbg13bb" "bcbg13bc" "bcbg13bd" "bcbg13be" ## [3228] "bcbg13ca" "bcbg13cb" "bcbg13cc" "bcbg13cd" "bcbg13ce" "bcbg14a" "bcbg14b" ## [3235] "bcbg14c" "bcbg14d" "bcbg14e" "bcbg14f" "bcbg14g" "bcbg14h" "bcbg14i" ## [3242] "bcbg14j" "bcbg14k" "bcbg15a" "bcbg15b" "bcbg15c" "bcbg15d" "bcbg15e" ## [3249] "bcbg15f" "bcbg15g" "bcbg15h" "bcbg16a" "bcbg16b" "bcbg16c" "bcbg16d" ## [3256] "bcbg16e" "bcbg16f" "bcbg16g" "bcbg16h" "bcbg16i" "bcbg16j" "bcbg16k" ## [3263] "bcbg17a" "bcbg17b" "bcbg18" "bcbg19" "bcbg20" "bcbg21a" "bcbg21b" ## [3270] "bcbg21c" "itlang_c" "lcid_c" "schwgt" "stotwgtu" "jkcrep" "jkczone" ## [3277] "bcbgdas" "bcbgeas" "bcbgmrs" "bcbgsrs" "bcdgdas" "bcdgeas" "bcdgmrs" ## [3284] "bcdgsrs" "bcdgsbc" "bcdgtihy" "idtealin" "idteach" "idlink" "idsubj" ## [3291] "itcourse" "matsubj" "scisubj" "nmteach" "nsteach" "nteach" "matwgt" ## [3298] "sciwgt" "tchwgt" "btbg01.math" "btbg02.math" "btbg03.math" "btbg04.math" "btbg05a.math" ## [3305] "btbg05b.math" "btbg05c.math" "btbg05d.math" "btbg05e.math" "btbg05f.math" "btbg05g.math" "btbg05h.math" ## [3312] "btbg05i.math" "btbg06a.math" "btbg06b.math" "btbg06c.math" "btbg06d.math" "btbg06e.math" "btbg06f.math" ## [3319] "btbg06g.math" "btbg06h.math" "btbg06i.math" "btbg06j.math" "btbg06k.math" "btbg06l.math" "btbg07a.math" ## [3326] "btbg07b.math" "btbg07c.math" "btbg07d.math" "btbg07e.math" "btbg07f.math" "btbg07g.math" "btbg07h.math" ## [3333] "btbg08a.math" "btbg08b.math" "btbg08c.math" "btbg08d.math" "btbg08e.math" "btbg09a.math" "btbg09b.math" ## [3340] "btbg09c.math" "btbg09d.math" "btbg09e.math" "btbg09f.math" "btbg09g.math" "btbg09h.math" "btbg10.math" ## [3347] "btbg11.math" "btbg12a.math" "btbg12b.math" "btbg12c.math" "btbg12d.math" "btbg12e.math" "btbg12f.math" ## [3354] "btbg12g.math" "btbg13a.math" "btbg13b.math" "btbg13c.math" "btbg13d.math" "btbg13e.math" "btbg13f.math" ## [3361] "btbg13g.math" "btbg13h.math" "btbm14.math" "btbm15a.math" "btbm15b.math" "btbm15c.math" "btbm15d.math" ## [3368] "btbm15e.math" "btbm15f.math" "btbm15g.math" "btbm15h.math" "btbm16.math" "btbm17a.math" "btbm17ba.math" ## [3375] "btbm17bb.math" "btbm17bc.math" "btbm17ca.math" "btbm17cb.math" "btbm17cc.math" "btbm17cd.math" "btbm18aa.math" ## [3382] "btbm18ab.math" "btbm18ac.math" "btbm18ba.math" "btbm18bb.math" "btbm18bc.math" "btbm18bd.math" "btbm18be.math" ## [3389] "btbm18bf.math" "btbm18bg.math" "btbm18ca.math" "btbm18cb.math" "btbm18cc.math" "btbm18cd.math" "btbm18ce.math" ## [3396] "btbm18cf.math" "btbm18da.math" "btbm18db.math" "btbm18dc.math" "btbm18dd.math" "btbm18de.math" "btbm18df.math" ## [3403] "btbm19a.math" "btbm19b.math" "btbm19ca.math" "btbm19cb.math" "btbm19cc.math" "btbm19cd.math" "btbm19ce.math" ## [3410] "btbm20a.math" "btbm20b.math" "btbm20c.math" "btbm20d.math" "btbm20e.math" "btbm21.math" "btbm22aa.math" ## [3417] "btbm22ba.math" "btbm22ab.math" "btbm22bb.math" "btbm22ac.math" "btbm22bc.math" "btbm22ad.math" "btbm22bd.math" ## [3424] "btbm22ae.math" "btbm22be.math" "btbm22af.math" "btbm22bf.math" "btbm22ag.math" "btbm22bg.math" "btbm23.math" ## [3431] "itlang_t.math" "lcid_t.math" "btbgeas.math" "btbglsn.math" "btbgsos.math" "btbgtjs.math" "btdgeas.math" ## [3438] "btdglsn.math" "btdgsos.math" "btdgtjs.math" "btdmmme.math" "btdmnum.math" "btdmalg.math" "btdmgeo.math" ## [3445] "btdmdat.math" "btbg01.sci" "btbg02.sci" "btbg03.sci" "btbg04.sci" "btbg05a.sci" "btbg05b.sci" ## [3452] "btbg05c.sci" "btbg05d.sci" "btbg05e.sci" "btbg05f.sci" "btbg05g.sci" "btbg05h.sci" "btbg05i.sci" ## [3459] "btbg06a.sci" "btbg06b.sci" "btbg06c.sci" "btbg06d.sci" "btbg06e.sci" "btbg06f.sci" "btbg06g.sci" ## [3466] "btbg06h.sci" "btbg06i.sci" "btbg06j.sci" "btbg06k.sci" "btbg06l.sci" "btbg07a.sci" "btbg07b.sci" ## [3473] "btbg07c.sci" "btbg07d.sci" "btbg07e.sci" "btbg07f.sci" "btbg07g.sci" "btbg07h.sci" "btbg08a.sci" ## [3480] "btbg08b.sci" "btbg08c.sci" "btbg08d.sci" "btbg08e.sci" "btbg09a.sci" "btbg09b.sci" "btbg09c.sci" ## [3487] "btbg09d.sci" "btbg09e.sci" "btbg09f.sci" "btbg09g.sci" "btbg09h.sci" "btbg10.sci" "btbg11.sci" ## [3494] "btbg12a.sci" "btbg12b.sci" "btbg12c.sci" "btbg12d.sci" "btbg12e.sci" "btbg12f.sci" "btbg12g.sci" ## [3501] "btbg13a.sci" "btbg13b.sci" "btbg13c.sci" "btbg13d.sci" "btbg13e.sci" "btbg13f.sci" "btbg13g.sci" ## [3508] "btbg13h.sci" "btbs14.sci" "btbs15a.sci" "btbs15b.sci" "btbs15c.sci" "btbs15d.sci" "btbs15e.sci" ## [3515] "btbs15f.sci" "btbs15g.sci" "btbs15h.sci" "btbs15i.sci" "btbs15j.sci" "btbs15k.sci" "btbs15l.sci" ## [3522] "btbs15m.sci" "btbs15n.sci" "btbs16a.sci" "btbs16ba.sci" "btbs16bb.sci" "btbs16bc.sci" "btbs16ca.sci" ## [3529] "btbs16cb.sci" "btbs16cc.sci" "btbs16cd.sci" "btbs17aa.sci" "btbs17ab.sci" "btbs17ac.sci" "btbs17ad.sci" ## [3536] "btbs17ae.sci" "btbs17af.sci" "btbs17ag.sci" "btbs17ba.sci" "btbs17bb.sci" "btbs17bc.sci" "btbs17bd.sci" ## [3543] "btbs17be.sci" "btbs17bf.sci" "btbs17bg.sci" "btbs17bh.sci" "btbs17ca.sci" "btbs17cb.sci" "btbs17cc.sci" ## [3550] "btbs17cd.sci" "btbs17ce.sci" "btbs17cf.sci" "btbs17cg.sci" "btbs17da.sci" "btbs17db.sci" "btbs17dc.sci" ## [3557] "btbs17dd.sci" "btbs18a.sci" "btbs18b.sci" "btbs18ca.sci" "btbs18cb.sci" "btbs18cc.sci" "btbs18cd.sci" ## [3564] "btbs18ce.sci" "btbs19a.sci" "btbs19b.sci" "btbs19c.sci" "btbs19d.sci" "btbs19e.sci" "btbs20.sci" ## [3571] "btbs21aa.sci" "btbs21ba.sci" "btbs21ab.sci" "btbs21bb.sci" "btbs21ac.sci" "btbs21bc.sci" "btbs21ad.sci" ## [3578] "btbs21bd.sci" "btbs21ae.sci" "btbs21be.sci" "btbs21af.sci" "btbs21bf.sci" "btbs21ag.sci" "btbs21bg.sci" ## [3585] "btbs22.sci" "itlang_t.sci" "lcid_t.sci" "btbgeas.sci" "btbglsn.sci" "btbgsos.sci" "btbgtjs.sci" ## [3592] "btbsesi.sci" "btdgeas.sci" "btdglsn.sci" "btdgsos.sci" "btdgtjs.sci" "btdsesi.sci" "btdsmse.sci" ## [3599] "btdsbio.sci" "btdsche.sci" "btdsphy.sci" "btdsear.sci" "jk.tchwgt1" "jk.tchwgt2" "jk.tchwgt3" ## [3606] "jk.tchwgt4" "jk.tchwgt5" "jk.tchwgt6" "jk.tchwgt7" "jk.tchwgt8" "jk.tchwgt9" "jk.tchwgt10" ## [3613] "jk.tchwgt11" "jk.tchwgt12" "jk.tchwgt13" "jk.tchwgt14" "jk.tchwgt15" "jk.tchwgt16" "jk.tchwgt17" ## [3620] "jk.tchwgt18" "jk.tchwgt19" "jk.tchwgt20" "jk.tchwgt21" "jk.tchwgt22" "jk.tchwgt23" "jk.tchwgt24" ## [3627] "jk.tchwgt25" "jk.tchwgt26" "jk.tchwgt27" "jk.tchwgt28" "jk.tchwgt29" "jk.tchwgt30" "jk.tchwgt31" ## [3634] "jk.tchwgt32" "jk.tchwgt33" "jk.tchwgt34" "jk.tchwgt35" "jk.tchwgt36" "jk.tchwgt37" "jk.tchwgt38" ## [3641] "jk.tchwgt39" "jk.tchwgt40" "jk.tchwgt41" "jk.tchwgt42" "jk.tchwgt43" "jk.tchwgt44" "jk.tchwgt45" ## [3648] "jk.tchwgt46" "jk.tchwgt47" "jk.tchwgt48" "jk.tchwgt49" "jk.tchwgt50" "jk.tchwgt51" "jk.tchwgt52" ## [3655] "jk.tchwgt53" "jk.tchwgt54" "jk.tchwgt55" "jk.tchwgt56" "jk.tchwgt57" "jk.tchwgt58" "jk.tchwgt59" ## [3662] "jk.tchwgt60" "jk.tchwgt61" "jk.tchwgt62" "jk.tchwgt63" "jk.tchwgt64" "jk.tchwgt65" "jk.tchwgt66" ## [3669] "jk.tchwgt67" "jk.tchwgt68" "jk.tchwgt69" "jk.tchwgt70" "jk.tchwgt71" "jk.tchwgt72" "jk.tchwgt73" ## [3676] "jk.tchwgt74" "jk.tchwgt75" "jk.tchwgt76" "jk.tchwgt77" "jk.tchwgt78" "jk.tchwgt79" "jk.tchwgt80" ## [3683] "jk.tchwgt81" "jk.tchwgt82" "jk.tchwgt83" "jk.tchwgt84" "jk.tchwgt85" "jk.tchwgt86" "jk.tchwgt87" ## [3690] "jk.tchwgt88" "jk.tchwgt89" "jk.tchwgt90" "jk.tchwgt91" "jk.tchwgt92" "jk.tchwgt93" "jk.tchwgt94" ## [3697] "jk.tchwgt95" "jk.tchwgt96" "jk.tchwgt97" "jk.tchwgt98" "jk.tchwgt99" "jk.tchwgt100" "jk.tchwgt101" ## [3704] "jk.tchwgt102" "jk.tchwgt103" "jk.tchwgt104" "jk.tchwgt105" "jk.tchwgt106" "jk.tchwgt107" "jk.tchwgt108" ## [3711] "jk.tchwgt109" "jk.tchwgt110" "jk.tchwgt111" "jk.tchwgt112" "jk.tchwgt113" "jk.tchwgt114" "jk.tchwgt115" ## [3718] "jk.tchwgt116" "jk.tchwgt117" "jk.tchwgt118" "jk.tchwgt119" "jk.tchwgt120" "jk.tchwgt121" "jk.tchwgt122" ## [3725] "jk.tchwgt123" "jk.tchwgt124" "jk.tchwgt125" "jk.tchwgt126" "jk.tchwgt127" "jk.tchwgt128" "jk.tchwgt129" ## [3732] "jk.tchwgt130" "jk.tchwgt131" "jk.tchwgt132" "jk.tchwgt133" "jk.tchwgt134" "jk.tchwgt135" "jk.tchwgt136" ## [3739] "jk.tchwgt137" "jk.tchwgt138" "jk.tchwgt139" "jk.tchwgt140" "jk.tchwgt141" "jk.tchwgt142" "jk.tchwgt143" ## [3746] "jk.tchwgt144" "jk.tchwgt145" "jk.tchwgt146" "jk.tchwgt147" "jk.tchwgt148" "jk.tchwgt149" "jk.tchwgt150" ## [3753] "jk.matwgt1" "jk.matwgt2" "jk.matwgt3" "jk.matwgt4" "jk.matwgt5" "jk.matwgt6" "jk.matwgt7" ## [3760] "jk.matwgt8" "jk.matwgt9" "jk.matwgt10" "jk.matwgt11" "jk.matwgt12" "jk.matwgt13" "jk.matwgt14" ## [3767] "jk.matwgt15" "jk.matwgt16" "jk.matwgt17" "jk.matwgt18" "jk.matwgt19" "jk.matwgt20" "jk.matwgt21" ## [3774] "jk.matwgt22" "jk.matwgt23" "jk.matwgt24" "jk.matwgt25" "jk.matwgt26" "jk.matwgt27" "jk.matwgt28" ## [3781] "jk.matwgt29" "jk.matwgt30" "jk.matwgt31" "jk.matwgt32" "jk.matwgt33" "jk.matwgt34" "jk.matwgt35" ## [3788] "jk.matwgt36" "jk.matwgt37" "jk.matwgt38" "jk.matwgt39" "jk.matwgt40" "jk.matwgt41" "jk.matwgt42" ## [3795] "jk.matwgt43" "jk.matwgt44" "jk.matwgt45" "jk.matwgt46" "jk.matwgt47" "jk.matwgt48" "jk.matwgt49" ## [3802] "jk.matwgt50" "jk.matwgt51" "jk.matwgt52" "jk.matwgt53" "jk.matwgt54" "jk.matwgt55" "jk.matwgt56" ## [3809] "jk.matwgt57" "jk.matwgt58" "jk.matwgt59" "jk.matwgt60" "jk.matwgt61" "jk.matwgt62" "jk.matwgt63" ## [3816] "jk.matwgt64" "jk.matwgt65" "jk.matwgt66" "jk.matwgt67" "jk.matwgt68" "jk.matwgt69" "jk.matwgt70" ## [3823] "jk.matwgt71" "jk.matwgt72" "jk.matwgt73" "jk.matwgt74" "jk.matwgt75" "jk.matwgt76" "jk.matwgt77" ## [3830] "jk.matwgt78" "jk.matwgt79" "jk.matwgt80" "jk.matwgt81" "jk.matwgt82" "jk.matwgt83" "jk.matwgt84" ## [3837] "jk.matwgt85" "jk.matwgt86" "jk.matwgt87" "jk.matwgt88" "jk.matwgt89" "jk.matwgt90" "jk.matwgt91" ## [3844] "jk.matwgt92" "jk.matwgt93" "jk.matwgt94" "jk.matwgt95" "jk.matwgt96" "jk.matwgt97" "jk.matwgt98" ## [3851] "jk.matwgt99" "jk.matwgt100" "jk.matwgt101" "jk.matwgt102" "jk.matwgt103" "jk.matwgt104" "jk.matwgt105" ## [3858] "jk.matwgt106" "jk.matwgt107" "jk.matwgt108" "jk.matwgt109" "jk.matwgt110" "jk.matwgt111" "jk.matwgt112" ## [3865] "jk.matwgt113" "jk.matwgt114" "jk.matwgt115" "jk.matwgt116" "jk.matwgt117" "jk.matwgt118" "jk.matwgt119" ## [3872] "jk.matwgt120" "jk.matwgt121" "jk.matwgt122" "jk.matwgt123" "jk.matwgt124" "jk.matwgt125" "jk.matwgt126" ## [3879] "jk.matwgt127" "jk.matwgt128" "jk.matwgt129" "jk.matwgt130" "jk.matwgt131" "jk.matwgt132" "jk.matwgt133" ## [3886] "jk.matwgt134" "jk.matwgt135" "jk.matwgt136" "jk.matwgt137" "jk.matwgt138" "jk.matwgt139" "jk.matwgt140" ## [3893] "jk.matwgt141" "jk.matwgt142" "jk.matwgt143" "jk.matwgt144" "jk.matwgt145" "jk.matwgt146" "jk.matwgt147" ## [3900] "jk.matwgt148" "jk.matwgt149" "jk.matwgt150" "jk.sciwgt1" "jk.sciwgt2" "jk.sciwgt3" "jk.sciwgt4" ## [3907] "jk.sciwgt5" "jk.sciwgt6" "jk.sciwgt7" "jk.sciwgt8" "jk.sciwgt9" "jk.sciwgt10" "jk.sciwgt11" ## [3914] "jk.sciwgt12" "jk.sciwgt13" "jk.sciwgt14" "jk.sciwgt15" "jk.sciwgt16" "jk.sciwgt17" "jk.sciwgt18" ## [3921] "jk.sciwgt19" "jk.sciwgt20" "jk.sciwgt21" "jk.sciwgt22" "jk.sciwgt23" "jk.sciwgt24" "jk.sciwgt25" ## [3928] "jk.sciwgt26" "jk.sciwgt27" "jk.sciwgt28" "jk.sciwgt29" "jk.sciwgt30" "jk.sciwgt31" "jk.sciwgt32" ## [3935] "jk.sciwgt33" "jk.sciwgt34" "jk.sciwgt35" "jk.sciwgt36" "jk.sciwgt37" "jk.sciwgt38" "jk.sciwgt39" ## [3942] "jk.sciwgt40" "jk.sciwgt41" "jk.sciwgt42" "jk.sciwgt43" "jk.sciwgt44" "jk.sciwgt45" "jk.sciwgt46" ## [3949] "jk.sciwgt47" "jk.sciwgt48" "jk.sciwgt49" "jk.sciwgt50" "jk.sciwgt51" "jk.sciwgt52" "jk.sciwgt53" ## [3956] "jk.sciwgt54" "jk.sciwgt55" "jk.sciwgt56" "jk.sciwgt57" "jk.sciwgt58" "jk.sciwgt59" "jk.sciwgt60" ## [3963] "jk.sciwgt61" "jk.sciwgt62" "jk.sciwgt63" "jk.sciwgt64" "jk.sciwgt65" "jk.sciwgt66" "jk.sciwgt67" ## [3970] "jk.sciwgt68" "jk.sciwgt69" "jk.sciwgt70" "jk.sciwgt71" "jk.sciwgt72" "jk.sciwgt73" "jk.sciwgt74" ## [3977] "jk.sciwgt75" "jk.sciwgt76" "jk.sciwgt77" "jk.sciwgt78" "jk.sciwgt79" "jk.sciwgt80" "jk.sciwgt81" ## [3984] "jk.sciwgt82" "jk.sciwgt83" "jk.sciwgt84" "jk.sciwgt85" "jk.sciwgt86" "jk.sciwgt87" "jk.sciwgt88" ## [3991] "jk.sciwgt89" "jk.sciwgt90" "jk.sciwgt91" "jk.sciwgt92" "jk.sciwgt93" "jk.sciwgt94" "jk.sciwgt95" ## [3998] "jk.sciwgt96" "jk.sciwgt97" "jk.sciwgt98" "jk.sciwgt99" "jk.sciwgt100" "jk.sciwgt101" "jk.sciwgt102" ## [4005] "jk.sciwgt103" "jk.sciwgt104" "jk.sciwgt105" "jk.sciwgt106" "jk.sciwgt107" "jk.sciwgt108" "jk.sciwgt109" ## [4012] "jk.sciwgt110" "jk.sciwgt111" "jk.sciwgt112" "jk.sciwgt113" "jk.sciwgt114" "jk.sciwgt115" "jk.sciwgt116" ## [4019] "jk.sciwgt117" "jk.sciwgt118" "jk.sciwgt119" "jk.sciwgt120" "jk.sciwgt121" "jk.sciwgt122" "jk.sciwgt123" ## [4026] "jk.sciwgt124" "jk.sciwgt125" "jk.sciwgt126" "jk.sciwgt127" "jk.sciwgt128" "jk.sciwgt129" "jk.sciwgt130" ## [4033] "jk.sciwgt131" "jk.sciwgt132" "jk.sciwgt133" "jk.sciwgt134" "jk.sciwgt135" "jk.sciwgt136" "jk.sciwgt137" ## [4040] "jk.sciwgt138" "jk.sciwgt139" "jk.sciwgt140" "jk.sciwgt141" "jk.sciwgt142" "jk.sciwgt143" "jk.sciwgt144" ## [4047] "jk.sciwgt145" "jk.sciwgt146" "jk.sciwgt147" "jk.sciwgt148" "jk.sciwgt149" "jk.sciwgt150" ``` ]] --- Meet Your Data - searchSDF ======================================================== **`searchSDF()`** - Search the survey data frame by character strings ```r searchSDF("education", ita) ``` ``` ## variableName Labels ## 1 bsbgher Home Educational Resources/SCL ## 2 bsdgher Home Educational Resources/IDX ## 3 bsdgedup Parents' Highest Education Level ## 4 bcbg20 GEN\\HIGHEST LEVEL OF FORMAL EDUCATION ## 5 bcbg21a GEN\\QUALIFICATIONS IN EDUCATIONAL LEADERSHIP\\<CERTIFICATE OR LICENSE> ## 6 bcbg21b GEN\\QUALIFICATIONS IN EDUCATIONAL LEADERSHIP\\ISCED 7 ## 7 bcbg21c GEN\\QUALIFICATIONS IN EDUCATIONAL LEADERSHIP\\ISCED 8 ## 8 btbg04.math GEN\\LEVEL OF FORMAL EDUCATION COMPLETED ## 9 btdmmme.math Teachers Majored in Mathematics and Mathematics Education ## 10 btbg04.sci GEN\\LEVEL OF FORMAL EDUCATION COMPLETED ## 11 btdsmse.sci Teachers Majored in Science and Science Education ``` - Add argument `levels = TRUE` to return variable levels. .codeScroll10[ ```r searchSDF("bsdgedup", ita, levels = TRUE) ``` ``` ## Variable: bsdgedup ## Label: Parents' Highest Education Level ## Levels (Lowest level first): ## 1. UNIVERSITY OR HIGHER ## 2. POST-SECONDARY BUT NOT UNIVERSITY ## 3. UPPER SECONDARY ## 4. LOWER SECONDARY ## 5. SOME PRIMARY, LOWER SECONDARY OR NO SCHOOL ## 6. DON'T KNOW ## 9. OMITTED OR INVALID ``` ] - What occurs with an empty string? ```r searchSDF("", ita) ``` --- Meet Your Data - levelsSDF ======================================================== **`levelsSDF()`** - Show the levels of a variable ```r levelsSDF("bsdgedup", ita) ``` ``` ## Levels for Variable 'bsdgedup' (Lowest level first): ## 1. UNIVERSITY OR HIGHER (n = 898) ## 2. POST-SECONDARY BUT NOT UNIVERSITY (n = 609) ## 3. UPPER SECONDARY (n = 1037) ## 4. LOWER SECONDARY (n = 410) ## 5. SOME PRIMARY, LOWER SECONDARY OR NO SCHOOL (n = 113) ## 6. DON'T KNOW (n = 478) ## 9. OMITTED OR INVALID* (n = 17) ## NOTE: * indicates an omitted level. ``` --- Meet Your Data - showCodebook ======================================================== **`showCodebook()`** - Show the levels of a variable ```r showCodebook(ita) ``` - `View()` shows a preview of a selected data set ```r View(showCodebook(ita)) ``` --- Meet Your Data - showPlausibleValues ======================================================== **`showPlausibleValues()`** - Prints all plausible values .codeScroll15[ ```r showPlausibleValues(ita) ``` ``` ## There are 16 subject scale(s) or subscale(s) in this edsurvey.data.frame: ## 'mmat' subject scale or subscale with 5 plausible values (the default). ## ## 'ssci' subject scale or subscale with 5 plausible values. ## ## 'malg' subject scale or subscale with 5 plausible values. ## ## 'mapp' subject scale or subscale with 5 plausible values. ## ## 'mdat' subject scale or subscale with 5 plausible values. ## ## 'mgeo' subject scale or subscale with 5 plausible values. ## ## 'mkno' subject scale or subscale with 5 plausible values. ## ## 'mnum' subject scale or subscale with 5 plausible values. ## ## 'mrea' subject scale or subscale with 5 plausible values. ## ## 'sapp' subject scale or subscale with 5 plausible values. ## ## 'sbio' subject scale or subscale with 5 plausible values. ## ## 'sche' subject scale or subscale with 5 plausible values. ## ## 'sear' subject scale or subscale with 5 plausible values. ## ## 'skno' subject scale or subscale with 5 plausible values. ## ## 'sphy' subject scale or subscale with 5 plausible values. ## ## 'srea' subject scale or subscale with 5 plausible values. ``` ] - add `verbose = TRUE` .codeScroll10[ ```r showPlausibleValues(ita, verbose = TRUE) ``` ``` ## There are 16 subject scale(s) or subscale(s) in this edsurvey.data.frame: ## 'mmat' subject scale or subscale with 5 plausible values (the default). ## The plausible value variables are: 'bsmmat01', 'bsmmat02', 'bsmmat03', 'bsmmat04', and 'bsmmat05' ## ## 'ssci' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsssci01', 'bsssci02', 'bsssci03', 'bsssci04', and 'bsssci05' ## ## 'malg' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsmalg01', 'bsmalg02', 'bsmalg03', 'bsmalg04', and 'bsmalg05' ## ## 'mapp' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsmapp01', 'bsmapp02', 'bsmapp03', 'bsmapp04', and 'bsmapp05' ## ## 'mdat' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsmdat01', 'bsmdat02', 'bsmdat03', 'bsmdat04', and 'bsmdat05' ## ## 'mgeo' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsmgeo01', 'bsmgeo02', 'bsmgeo03', 'bsmgeo04', and 'bsmgeo05' ## ## 'mkno' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsmkno01', 'bsmkno02', 'bsmkno03', 'bsmkno04', and 'bsmkno05' ## ## 'mnum' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsmnum01', 'bsmnum02', 'bsmnum03', 'bsmnum04', and 'bsmnum05' ## ## 'mrea' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsmrea01', 'bsmrea02', 'bsmrea03', 'bsmrea04', and 'bsmrea05' ## ## 'sapp' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bssapp01', 'bssapp02', 'bssapp03', 'bssapp04', and 'bssapp05' ## ## 'sbio' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bssbio01', 'bssbio02', 'bssbio03', 'bssbio04', and 'bssbio05' ## ## 'sche' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bssche01', 'bssche02', 'bssche03', 'bssche04', and 'bssche05' ## ## 'sear' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bssear01', 'bssear02', 'bssear03', 'bssear04', and 'bssear05' ## ## 'skno' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bsskno01', 'bsskno02', 'bsskno03', 'bsskno04', and 'bsskno05' ## ## 'sphy' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bssphy01', 'bssphy02', 'bssphy03', 'bssphy04', and 'bssphy05' ## ## 'srea' subject scale or subscale with 5 plausible values. ## The plausible value variables are: 'bssrea01', 'bssrea02', 'bssrea03', 'bssrea04', and 'bssrea05' ``` ] --- Meet Your Data - showWeights ======================================================== **`showWeights()`** - Prints all weights: ```r showWeights(ita) ``` ``` ## There are 4 full sample weights in this edsurvey.data.frame: ## 'totwgt' with 150 JK replicate weights (the default). ## ## 'tchwgt' with 150 JK replicate weights. ## ## 'matwgt' with 150 JK replicate weights. ## ## 'sciwgt' with 150 JK replicate weights. ``` - add `verbose = TRUE` to print the complete list of jackknife replicate weights associated with each full sample weight. ```r showWeights(ita, verbose = TRUE) ``` ``` ## There are 4 full sample weights in this edsurvey.data.frame: ## 'totwgt' with 150 JK replicate weights (the default). ## Jackknife replicate weight variables associated with the full sample weight 'totwgt': ## 'jk1', 'jk2', 'jk3', 'jk4', 'jk5', 'jk6', 'jk7', 'jk8', 'jk9', 'jk10', 'jk11', 'jk12', 'jk13', 'jk14', 'jk15', ## 'jk16', 'jk17', 'jk18', 'jk19', 'jk20', 'jk21', 'jk22', 'jk23', 'jk24', 'jk25', 'jk26', 'jk27', 'jk28', 'jk29', ## 'jk30', 'jk31', 'jk32', 'jk33', 'jk34', 'jk35', 'jk36', 'jk37', 'jk38', 'jk39', 'jk40', 'jk41', 'jk42', 'jk43', ## 'jk44', 'jk45', 'jk46', 'jk47', 'jk48', 'jk49', 'jk50', 'jk51', 'jk52', 'jk53', 'jk54', 'jk55', 'jk56', 'jk57', ## 'jk58', 'jk59', 'jk60', 'jk61', 'jk62', 'jk63', 'jk64', 'jk65', 'jk66', 'jk67', 'jk68', 'jk69', 'jk70', 'jk71', ## 'jk72', 'jk73', 'jk74', 'jk75', 'jk76', 'jk77', 'jk78', 'jk79', 'jk80', 'jk81', 'jk82', 'jk83', 'jk84', 'jk85', ## 'jk86', 'jk87', 'jk88', 'jk89', 'jk90', 'jk91', 'jk92', 'jk93', 'jk94', 'jk95', 'jk96', 'jk97', 'jk98', 'jk99', ## 'jk100', 'jk101', 'jk102', 'jk103', 'jk104', 'jk105', 'jk106', 'jk107', 'jk108', 'jk109', 'jk110', 'jk111', ## 'jk112', 'jk113', 'jk114', 'jk115', 'jk116', 'jk117', 'jk118', 'jk119', 'jk120', 'jk121', 'jk122', 'jk123', ## 'jk124', 'jk125', 'jk126', 'jk127', 'jk128', 'jk129', 'jk130', 'jk131', 'jk132', 'jk133', 'jk134', 'jk135', ## 'jk136', 'jk137', 'jk138', 'jk139', 'jk140', 'jk141', 'jk142', 'jk143', 'jk144', 'jk145', 'jk146', 'jk147', ## 'jk148', 'jk149', and 'jk150' ## ## 'tchwgt' with 150 JK replicate weights. ## Jackknife replicate weight variables associated with the full sample weight 'tchwgt': ## 'jk.tchwgt1', 'jk.tchwgt2', 'jk.tchwgt3', 'jk.tchwgt4', 'jk.tchwgt5', 'jk.tchwgt6', 'jk.tchwgt7', 'jk.tchwgt8', ## 'jk.tchwgt9', 'jk.tchwgt10', 'jk.tchwgt11', 'jk.tchwgt12', 'jk.tchwgt13', 'jk.tchwgt14', 'jk.tchwgt15', ## 'jk.tchwgt16', 'jk.tchwgt17', 'jk.tchwgt18', 'jk.tchwgt19', 'jk.tchwgt20', 'jk.tchwgt21', 'jk.tchwgt22', ## 'jk.tchwgt23', 'jk.tchwgt24', 'jk.tchwgt25', 'jk.tchwgt26', 'jk.tchwgt27', 'jk.tchwgt28', 'jk.tchwgt29', ## 'jk.tchwgt30', 'jk.tchwgt31', 'jk.tchwgt32', 'jk.tchwgt33', 'jk.tchwgt34', 'jk.tchwgt35', 'jk.tchwgt36', ## 'jk.tchwgt37', 'jk.tchwgt38', 'jk.tchwgt39', 'jk.tchwgt40', 'jk.tchwgt41', 'jk.tchwgt42', 'jk.tchwgt43', ## 'jk.tchwgt44', 'jk.tchwgt45', 'jk.tchwgt46', 'jk.tchwgt47', 'jk.tchwgt48', 'jk.tchwgt49', 'jk.tchwgt50', ## 'jk.tchwgt51', 'jk.tchwgt52', 'jk.tchwgt53', 'jk.tchwgt54', 'jk.tchwgt55', 'jk.tchwgt56', 'jk.tchwgt57', ## 'jk.tchwgt58', 'jk.tchwgt59', 'jk.tchwgt60', 'jk.tchwgt61', 'jk.tchwgt62', 'jk.tchwgt63', 'jk.tchwgt64', ## 'jk.tchwgt65', 'jk.tchwgt66', 'jk.tchwgt67', 'jk.tchwgt68', 'jk.tchwgt69', 'jk.tchwgt70', 'jk.tchwgt71', ## 'jk.tchwgt72', 'jk.tchwgt73', 'jk.tchwgt74', 'jk.tchwgt75', 'jk.tchwgt76', 'jk.tchwgt77', 'jk.tchwgt78', ## 'jk.tchwgt79', 'jk.tchwgt80', 'jk.tchwgt81', 'jk.tchwgt82', 'jk.tchwgt83', 'jk.tchwgt84', 'jk.tchwgt85', ## 'jk.tchwgt86', 'jk.tchwgt87', 'jk.tchwgt88', 'jk.tchwgt89', 'jk.tchwgt90', 'jk.tchwgt91', 'jk.tchwgt92', ## 'jk.tchwgt93', 'jk.tchwgt94', 'jk.tchwgt95', 'jk.tchwgt96', 'jk.tchwgt97', 'jk.tchwgt98', 'jk.tchwgt99', ## 'jk.tchwgt100', 'jk.tchwgt101', 'jk.tchwgt102', 'jk.tchwgt103', 'jk.tchwgt104', 'jk.tchwgt105', 'jk.tchwgt106', ## 'jk.tchwgt107', 'jk.tchwgt108', 'jk.tchwgt109', 'jk.tchwgt110', 'jk.tchwgt111', 'jk.tchwgt112', 'jk.tchwgt113', ## 'jk.tchwgt114', 'jk.tchwgt115', 'jk.tchwgt116', 'jk.tchwgt117', 'jk.tchwgt118', 'jk.tchwgt119', 'jk.tchwgt120', ## 'jk.tchwgt121', 'jk.tchwgt122', 'jk.tchwgt123', 'jk.tchwgt124', 'jk.tchwgt125', 'jk.tchwgt126', 'jk.tchwgt127', ## 'jk.tchwgt128', 'jk.tchwgt129', 'jk.tchwgt130', 'jk.tchwgt131', 'jk.tchwgt132', 'jk.tchwgt133', 'jk.tchwgt134', ## 'jk.tchwgt135', 'jk.tchwgt136', 'jk.tchwgt137', 'jk.tchwgt138', 'jk.tchwgt139', 'jk.tchwgt140', 'jk.tchwgt141', ## 'jk.tchwgt142', 'jk.tchwgt143', 'jk.tchwgt144', 'jk.tchwgt145', 'jk.tchwgt146', 'jk.tchwgt147', 'jk.tchwgt148', ## 'jk.tchwgt149', and 'jk.tchwgt150' ## ## 'matwgt' with 150 JK replicate weights. ## Jackknife replicate weight variables associated with the full sample weight 'matwgt': ## 'jk.matwgt1', 'jk.matwgt2', 'jk.matwgt3', 'jk.matwgt4', 'jk.matwgt5', 'jk.matwgt6', 'jk.matwgt7', 'jk.matwgt8', ## 'jk.matwgt9', 'jk.matwgt10', 'jk.matwgt11', 'jk.matwgt12', 'jk.matwgt13', 'jk.matwgt14', 'jk.matwgt15', ## 'jk.matwgt16', 'jk.matwgt17', 'jk.matwgt18', 'jk.matwgt19', 'jk.matwgt20', 'jk.matwgt21', 'jk.matwgt22', ## 'jk.matwgt23', 'jk.matwgt24', 'jk.matwgt25', 'jk.matwgt26', 'jk.matwgt27', 'jk.matwgt28', 'jk.matwgt29', ## 'jk.matwgt30', 'jk.matwgt31', 'jk.matwgt32', 'jk.matwgt33', 'jk.matwgt34', 'jk.matwgt35', 'jk.matwgt36', ## 'jk.matwgt37', 'jk.matwgt38', 'jk.matwgt39', 'jk.matwgt40', 'jk.matwgt41', 'jk.matwgt42', 'jk.matwgt43', ## 'jk.matwgt44', 'jk.matwgt45', 'jk.matwgt46', 'jk.matwgt47', 'jk.matwgt48', 'jk.matwgt49', 'jk.matwgt50', ## 'jk.matwgt51', 'jk.matwgt52', 'jk.matwgt53', 'jk.matwgt54', 'jk.matwgt55', 'jk.matwgt56', 'jk.matwgt57', ## 'jk.matwgt58', 'jk.matwgt59', 'jk.matwgt60', 'jk.matwgt61', 'jk.matwgt62', 'jk.matwgt63', 'jk.matwgt64', ## 'jk.matwgt65', 'jk.matwgt66', 'jk.matwgt67', 'jk.matwgt68', 'jk.matwgt69', 'jk.matwgt70', 'jk.matwgt71', ## 'jk.matwgt72', 'jk.matwgt73', 'jk.matwgt74', 'jk.matwgt75', 'jk.matwgt76', 'jk.matwgt77', 'jk.matwgt78', ## 'jk.matwgt79', 'jk.matwgt80', 'jk.matwgt81', 'jk.matwgt82', 'jk.matwgt83', 'jk.matwgt84', 'jk.matwgt85', ## 'jk.matwgt86', 'jk.matwgt87', 'jk.matwgt88', 'jk.matwgt89', 'jk.matwgt90', 'jk.matwgt91', 'jk.matwgt92', ## 'jk.matwgt93', 'jk.matwgt94', 'jk.matwgt95', 'jk.matwgt96', 'jk.matwgt97', 'jk.matwgt98', 'jk.matwgt99', ## 'jk.matwgt100', 'jk.matwgt101', 'jk.matwgt102', 'jk.matwgt103', 'jk.matwgt104', 'jk.matwgt105', 'jk.matwgt106', ## 'jk.matwgt107', 'jk.matwgt108', 'jk.matwgt109', 'jk.matwgt110', 'jk.matwgt111', 'jk.matwgt112', 'jk.matwgt113', ## 'jk.matwgt114', 'jk.matwgt115', 'jk.matwgt116', 'jk.matwgt117', 'jk.matwgt118', 'jk.matwgt119', 'jk.matwgt120', ## 'jk.matwgt121', 'jk.matwgt122', 'jk.matwgt123', 'jk.matwgt124', 'jk.matwgt125', 'jk.matwgt126', 'jk.matwgt127', ## 'jk.matwgt128', 'jk.matwgt129', 'jk.matwgt130', 'jk.matwgt131', 'jk.matwgt132', 'jk.matwgt133', 'jk.matwgt134', ## 'jk.matwgt135', 'jk.matwgt136', 'jk.matwgt137', 'jk.matwgt138', 'jk.matwgt139', 'jk.matwgt140', 'jk.matwgt141', ## 'jk.matwgt142', 'jk.matwgt143', 'jk.matwgt144', 'jk.matwgt145', 'jk.matwgt146', 'jk.matwgt147', 'jk.matwgt148', ## 'jk.matwgt149', and 'jk.matwgt150' ## ## 'sciwgt' with 150 JK replicate weights. ## Jackknife replicate weight variables associated with the full sample weight 'sciwgt': ## 'jk.sciwgt1', 'jk.sciwgt2', 'jk.sciwgt3', 'jk.sciwgt4', 'jk.sciwgt5', 'jk.sciwgt6', 'jk.sciwgt7', 'jk.sciwgt8', ## 'jk.sciwgt9', 'jk.sciwgt10', 'jk.sciwgt11', 'jk.sciwgt12', 'jk.sciwgt13', 'jk.sciwgt14', 'jk.sciwgt15', ## 'jk.sciwgt16', 'jk.sciwgt17', 'jk.sciwgt18', 'jk.sciwgt19', 'jk.sciwgt20', 'jk.sciwgt21', 'jk.sciwgt22', ## 'jk.sciwgt23', 'jk.sciwgt24', 'jk.sciwgt25', 'jk.sciwgt26', 'jk.sciwgt27', 'jk.sciwgt28', 'jk.sciwgt29', ## 'jk.sciwgt30', 'jk.sciwgt31', 'jk.sciwgt32', 'jk.sciwgt33', 'jk.sciwgt34', 'jk.sciwgt35', 'jk.sciwgt36', ## 'jk.sciwgt37', 'jk.sciwgt38', 'jk.sciwgt39', 'jk.sciwgt40', 'jk.sciwgt41', 'jk.sciwgt42', 'jk.sciwgt43', ## 'jk.sciwgt44', 'jk.sciwgt45', 'jk.sciwgt46', 'jk.sciwgt47', 'jk.sciwgt48', 'jk.sciwgt49', 'jk.sciwgt50', ## 'jk.sciwgt51', 'jk.sciwgt52', 'jk.sciwgt53', 'jk.sciwgt54', 'jk.sciwgt55', 'jk.sciwgt56', 'jk.sciwgt57', ## 'jk.sciwgt58', 'jk.sciwgt59', 'jk.sciwgt60', 'jk.sciwgt61', 'jk.sciwgt62', 'jk.sciwgt63', 'jk.sciwgt64', ## 'jk.sciwgt65', 'jk.sciwgt66', 'jk.sciwgt67', 'jk.sciwgt68', 'jk.sciwgt69', 'jk.sciwgt70', 'jk.sciwgt71', ## 'jk.sciwgt72', 'jk.sciwgt73', 'jk.sciwgt74', 'jk.sciwgt75', 'jk.sciwgt76', 'jk.sciwgt77', 'jk.sciwgt78', ## 'jk.sciwgt79', 'jk.sciwgt80', 'jk.sciwgt81', 'jk.sciwgt82', 'jk.sciwgt83', 'jk.sciwgt84', 'jk.sciwgt85', ## 'jk.sciwgt86', 'jk.sciwgt87', 'jk.sciwgt88', 'jk.sciwgt89', 'jk.sciwgt90', 'jk.sciwgt91', 'jk.sciwgt92', ## 'jk.sciwgt93', 'jk.sciwgt94', 'jk.sciwgt95', 'jk.sciwgt96', 'jk.sciwgt97', 'jk.sciwgt98', 'jk.sciwgt99', ## 'jk.sciwgt100', 'jk.sciwgt101', 'jk.sciwgt102', 'jk.sciwgt103', 'jk.sciwgt104', 'jk.sciwgt105', 'jk.sciwgt106', ## 'jk.sciwgt107', 'jk.sciwgt108', 'jk.sciwgt109', 'jk.sciwgt110', 'jk.sciwgt111', 'jk.sciwgt112', 'jk.sciwgt113', ## 'jk.sciwgt114', 'jk.sciwgt115', 'jk.sciwgt116', 'jk.sciwgt117', 'jk.sciwgt118', 'jk.sciwgt119', 'jk.sciwgt120', ## 'jk.sciwgt121', 'jk.sciwgt122', 'jk.sciwgt123', 'jk.sciwgt124', 'jk.sciwgt125', 'jk.sciwgt126', 'jk.sciwgt127', ## 'jk.sciwgt128', 'jk.sciwgt129', 'jk.sciwgt130', 'jk.sciwgt131', 'jk.sciwgt132', 'jk.sciwgt133', 'jk.sciwgt134', ## 'jk.sciwgt135', 'jk.sciwgt136', 'jk.sciwgt137', 'jk.sciwgt138', 'jk.sciwgt139', 'jk.sciwgt140', 'jk.sciwgt141', ## 'jk.sciwgt142', 'jk.sciwgt143', 'jk.sciwgt144', 'jk.sciwgt145', 'jk.sciwgt146', 'jk.sciwgt147', 'jk.sciwgt148', ## 'jk.sciwgt149', and 'jk.sciwgt150' ``` --- Meet Your Data - Omitted Levels ======================================================== - Levels of the variables that will be omitted by default from the `edsurvey.data.frame` <!-- --> --- Meet Your Data - Default Conditions ======================================================== - Special considerations for a particular `edsurvey.data.frame` <!-- --> --- class: inverse, section-slide Data Manipulation ======================================================== <hr class="sectionSlide"> .logoSection[ <img src="data:image/png;base64,#presentationFigures/naepLogo.png" width="80%" height="80%" /> ] --- EdSurvey Calls Network Connection ======================================================== **Small Memory Footprint** <img src="data:image/png;base64,#presentationFigures/data_footprint.png" width="200%" height="200%" /> --- Data Manipulation - getData ======================================================== **`getData()`:** reads in selected variables and sampling weights from the EdSurvey database and returns a `light.EdSurvey.data.frame` (a data frame like object) into the Global environment. *Functionality* - Retrieve variables by call. - Manipulate the resulting `light.EdSurvey.data.frame`: - Subset. - Recode. - Drop levels. - Use `EdSurvey` package functions on `light.EdSurvey.data.frames`. - Related Documentation - [getData.pdf](https://www.air.org/sites/default/files/EdSurvey-getData.pdf), [Chap 9, EdSurvey Book](https://naep-research.airprojects.org/Portals/0/EdSurvey_A_Users_Guide/_book/analysisOutsideEdSurvey.html) --- Data Manipulation - getData ======================================================== **`getData()`** ```r gddat <- getData(ita, varnames = c('bsdgedup', 'mmat', 'totwgt'), addAttributes = TRUE, omittedLevels = FALSE) ``` NAEP mathematics composite scale scores of 8th grade students - A vector of variable names, including `bsdgedup` (parent's education) - Overall math performance in math (`mmat`) - The sampling weight for this dataframe: `totwgt` --- Data Manipulation - getData ======================================================== Output: .codeScroll25[ ```r # Note: head returns the first 6 rows of a data frame head(gddat) ``` ``` ## bsdgedup bsmmat01 bsmmat02 bsmmat03 bsmmat04 bsmmat05 jk1 jk2 jk3 jk4 jk5 jk6 ## 1 <NA> 526.4047 464.2095 475.1649 506.7626 471.8710 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 UNIVERSITY OR HIGHER 491.0990 525.1049 502.4696 502.3863 512.1954 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 LOWER SECONDARY 415.1943 377.6756 345.3367 417.2805 348.1346 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 UPPER SECONDARY 481.0130 477.9657 404.6423 461.9171 491.6918 113.64525 113.64525 0.00000 227.29050 113.64525 113.64525 ## 5 UNIVERSITY OR HIGHER 476.5554 526.6320 503.7531 506.0059 518.1913 100.48633 100.48633 0.00000 200.97265 100.48633 100.48633 ## 6 LOWER SECONDARY 520.8776 535.6901 496.9486 484.0057 499.6926 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk7 jk8 jk9 jk10 jk11 jk12 jk13 jk14 jk15 jk16 jk17 jk18 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk19 jk20 jk21 jk22 jk23 jk24 jk25 jk26 jk27 jk28 jk29 jk30 ## 1 216.33150 216.33150 216.33150 216.33150 0.00000 432.66300 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 0.00000 432.66300 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk31 jk32 jk33 jk34 jk35 jk36 jk37 jk38 jk39 jk40 jk41 jk42 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.3315 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.3315 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.6089 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.6452 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.4863 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 0.0000 25.69602 12.84801 12.84801 ## jk43 jk44 jk45 jk46 jk47 jk48 jk49 jk50 jk51 jk52 jk53 jk54 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk55 jk56 jk57 jk58 jk59 jk60 jk61 jk62 jk63 jk64 jk65 jk66 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk67 jk68 jk69 jk70 jk71 jk72 jk73 jk74 jk75 jk76 jk77 jk78 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk79 jk80 jk81 jk82 jk83 jk84 jk85 jk86 jk87 jk88 jk89 jk90 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk91 jk92 jk93 jk94 jk95 jk96 jk97 jk98 jk99 jk100 jk101 jk102 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk103 jk104 jk105 jk106 jk107 jk108 jk109 jk110 jk111 jk112 jk113 jk114 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk115 jk116 jk117 jk118 jk119 jk120 jk121 jk122 jk123 jk124 jk125 jk126 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk127 jk128 jk129 jk130 jk131 jk132 jk133 jk134 jk135 jk136 jk137 jk138 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 0.00000 329.21773 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## jk139 jk140 jk141 jk142 jk143 jk144 jk145 jk146 jk147 jk148 jk149 jk150 ## 1 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 2 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 216.33150 ## 3 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 164.60887 ## 4 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 113.64525 ## 5 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 100.48633 ## 6 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 12.84801 ## totwgt ## 1 216.33150 ## 2 216.33150 ## 3 164.60887 ## 4 113.64525 ## 5 100.48633 ## 6 12.84801 ``` ] --- Data Manipulation - getData ======================================================== **`getData()`** ```r gddat <- getData(ita, varnames = c('bsdgedup', 'mmat', 'totwgt'), addAttributes = TRUE, omittedLevels = FALSE) ``` - A few important things to note: - "`addAttributes = TRUE`" allows the data.frame to be passed to `EdSurvey` package functions - all of the jackknife replicates are automatically returned (`srwt01` to `srwt62`) - "`omittedLevels = FALSE`" returns variables with special values (such as multiple entries or NA's) and manipulated by the user --- Data Manipulation - subset ======================================================== **`subset()`**: Returns only the data matching elements from a variable - Subset the connection to the data for all analyses: ```r ita_females <- subset(ita, itsex %in% c("FEMALE")) ``` - As expected the `subsetSDF` contains about half of the rows as the original: ```r dim(ita) ``` ``` ## [1] 7238 4052 ``` ```r dim(ita_females) ``` ``` ## [1] 3580 4052 ``` --- Data Manipulation - recode.sdf ======================================================== **`recode.sdf()`** is used to recode the levels of a variable - collapse or rename values ```r ita$books <- ifelse(ita$bsbg04 %in% c("NONE OR VERY FEW (0-10 BOOKS)", "ENOUGH TO FILL ONE SHELF (11-25 BOOKS)"), "0-25", as.character(ita$bsbg04)) table(ita$books, ita$bsbg04) ``` ``` ## ## NONE OR VERY FEW (0-10 BOOKS) ENOUGH TO FILL ONE SHELF (11-25 BOOKS) ## 0-25 600 901 ## ENOUGH TO FILL ONE BOOKCASE (26-100 BOOKS) 0 0 ## ENOUGH TO FILL THREE OR MORE BOOKCASES (MORE THAN 200) 0 0 ## ENOUGH TO FILL TWO BOOKCASES (101-200 BOOKS) 0 0 ## OMITTED OR INVALID 0 0 ## ## ENOUGH TO FILL ONE BOOKCASE (26-100 BOOKS) ## 0-25 0 ## ENOUGH TO FILL ONE BOOKCASE (26-100 BOOKS) 872 ## ENOUGH TO FILL THREE OR MORE BOOKCASES (MORE THAN 200) 0 ## ENOUGH TO FILL TWO BOOKCASES (101-200 BOOKS) 0 ## OMITTED OR INVALID 0 ## ## ENOUGH TO FILL TWO BOOKCASES (101-200 BOOKS) ## 0-25 0 ## ENOUGH TO FILL ONE BOOKCASE (26-100 BOOKS) 0 ## ENOUGH TO FILL THREE OR MORE BOOKCASES (MORE THAN 200) 0 ## ENOUGH TO FILL TWO BOOKCASES (101-200 BOOKS) 542 ## OMITTED OR INVALID 0 ## ## ENOUGH TO FILL THREE OR MORE BOOKCASES (MORE THAN 200) ## 0-25 0 ## ENOUGH TO FILL ONE BOOKCASE (26-100 BOOKS) 0 ## ENOUGH TO FILL THREE OR MORE BOOKCASES (MORE THAN 200) 692 ## ENOUGH TO FILL TWO BOOKCASES (101-200 BOOKS) 0 ## OMITTED OR INVALID 0 ## ## OMITTED OR INVALID ## 0-25 0 ## ENOUGH TO FILL ONE BOOKCASE (26-100 BOOKS) 0 ## ENOUGH TO FILL THREE OR MORE BOOKCASES (MORE THAN 200) 0 ## ENOUGH TO FILL TWO BOOKCASES (101-200 BOOKS) 0 ## OMITTED OR INVALID 4 ``` --- Data Manipulation - apply ======================================================== **`apply`** will take the matrix of all the visits and do something to them at the student level. ```r res <- searchSDF(c("visits", "screen"), ita) ita$visits <- apply(ita[,res$variableName], 1, sum, na.rm=TRUE) ``` **`sum`** says to add them up at the student level **`na.rm`** gets passed to `sum` and tells `sum` to ignore `NA` - how can you visualize the distribution of visits?